L'Ateneu
L'Ateneu d'Innovació Digital i Democràtica
Changes at "Anàlisi i Teoria de Xarxes Socials"
Description (Català)
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Una introducció neta i ràpida
L'anàlisi de xarxes socials és una eina teòrica i metodològica per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.
En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals. Acompanyarem el recorregut teòric i metodològic amb sessions pràctiques per a aprendre a dominar eines d'anàlisis i visualització de xarxes com Gephi i altres.
Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horaris i inscripcions
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- Sessions presencials.
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- Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.
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- Inscriu-te en aquesta mateixa pàgina, o clicant aquí. Quedaràs inscrit als quatre dies que dura la formació!
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Primera sessió: Introducció al pensament de xarxa
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- 11 de maig
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- Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament.
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Segona sessió: Hands-on! Instal·lació d’eines
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- 18 de maig
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- Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.
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Tercera sessió: Anàlisi i modelització de xarxes
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- 25 de maig
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- A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.
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Quarta sessió: Social Big Data i anàlisi de xarxes socials
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- 31 de maig (dilluns)
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- Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.
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>> Places exhaurides! Apunta't a la llista d'espera <<
Una introducció neta i ràpida
L'anàlisi de xarxes socials és una eina teòrica i metodològica per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.
En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals. Acompanyarem el recorregut teòric i metodològic amb sessions pràctiques per a aprendre a dominar eines d'anàlisis i visualització de xarxes com Gephi i altres.
Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horaris i inscripcions
- Sessions presencials.
- Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.
- Inscriu-te en aquesta mateixa pàgina, o clicant aquí. Quedaràs inscrit als quatre dies que dura la formació!
Primera sessió: Introducció al pensament de xarxa
- 11 de maig
- Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament.
Segona sessió: Hands-on! Instal·lació d’eines
- 18 de maig
- Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.
Tercera sessió: Anàlisi i modelització de xarxes
- 25 de maig
- A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.
Quarta sessió: Social Big Data i anàlisi de xarxes socials
- 31 de maig (dilluns)
- Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.
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Apunta't a la llista d'espera</strong></a><strong> <<</strong></p><p><img 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"></p><h5><strong>Una introducció neta i ràpida</strong></h5><p>L'anàlisi de xarxes socials és una <strong>eina teòrica i metodològica</strong> per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.</p><p>En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó <strong>com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals</strong>. Acompanyarem el <strong>recorregut teòric i metodològic</strong> amb <strong>sessions</strong> <strong>pràctiques</strong> per a aprendre a dominar eines d'anàlisis i visualització de xarxes com <em>Gephi</em> i altres. </p><p>Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img src="data:image/png;base64,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"><img 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"></p><h5><strong>Horaris i inscripcions</strong></h5><ul>-<li>Sessions presencials.</li>-<li>Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.</li>-<li>Inscriu-te en aquesta mateixa pàgina, o clicant <a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/1947/meetings/2184/registration/join" target="_blank">aquí</a>. Quedaràs inscrit als quatre dies que dura la formació!</li>-</ul><h5><strong>Primera sessió: Introducció al pensament de xarxa</strong></h5><ul>-<li>11 de maig</li>-<li>Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament. </li>-</ul><h5><strong>Segona sessió: <em>Hands-on</em>! Instal·lació d’eines</strong></h5><ul>-<li>18 de maig </li>-<li>Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.</li>-</ul><h5><strong>Tercera sessió: Anàlisi i modelització de xarxes</strong></h5><ul>-<li>25 de maig</li>-<li>A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.</li>-</ul><h5><strong>Quarta sessió: <em>Social Big Data </em>i anàlisi de xarxes socials</strong></h5><ul>-<li>31 de maig (dilluns)</li>-<li>Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.</li>-</ul>- +<p><strong>>> </strong><a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/2030/" target="_blank"><strong>Places exhaurides! Apunta't a la llista d'espera</strong></a><strong> <<</strong></p><p><img src="data:image/png;base64,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"></p><h5><strong>Una introducció neta i ràpida</strong></h5><p>L'anàlisi de xarxes socials és una <strong>eina teòrica i metodològica</strong> per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.</p><p>En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó <strong>com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals</strong>. Acompanyarem el <strong>recorregut teòric i metodològic</strong> amb <strong>sessions</strong> <strong>pràctiques</strong> per a aprendre a dominar eines d'anàlisis i visualització de xarxes com <em>Gephi</em> i altres. </p><p>Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img src="data:image/png;base64,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"><img 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"></p><h5><strong>Horaris i inscripcions</strong></h5><ul><li>Sessions presencials.</li><li>Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.</li><li>Inscriu-te en aquesta mateixa pàgina, o clicant <a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/1947/meetings/2184/registration/join" target="_blank">aquí</a>. Quedaràs inscrit als quatre dies que dura la formació!</li></ul><h5><a href="https://diode.zone/videos/watch/4ca4d8be-479e-4c6f-9b53-bdcd0ee10078" target="_blank"><strong>Primera sessió: Introducció al pensament de xarxa</strong></a></h5><ul><li>11 de maig</li><li>Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament. </li></ul><h5><strong>Segona sessió: <em>Hands-on</em>! Instal·lació d’eines</strong></h5><ul><li>18 de maig </li><li>Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.</li></ul><h5><a href="https://diode.zone/videos/watch/86ce06ea-b24b-4841-8409-52ef70655b35" target="_blank"><strong>Tercera sessió: Anàlisi i modelització de xarxes</strong></a></h5><ul><li>25 de maig</li><li>A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.</li></ul><h5><a href="https://diode.zone/videos/watch/2deae660-72d8-421a-8da2-efdbc1904648" target="_blank"><strong>Quarta sessió: <em>Social Big Data </em>i anàlisi de xarxes socials</strong></a></h5><ul><li>31 de maig (dilluns)</li><li>Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.</li></ul>
-
-
>> Places exhaurides! Apunta't a la llista d'espera <<
Una introducció neta i ràpida
L'anàlisi de xarxes socials és una eina teòrica i metodològica per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.
En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals. Acompanyarem el recorregut teòric i metodològic amb sessions pràctiques per a aprendre a dominar eines d'anàlisis i visualització de xarxes com Gephi i altres.
Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horaris i inscripcions
- -
- Sessions presencials.
- -
- Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.
- -
- Inscriu-te en aquesta mateixa pàgina, o clicant aquí. Quedaràs inscrit als quatre dies que dura la formació!
- -
Primera sessió: Introducció al pensament de xarxa
- -
- 11 de maig
- -
- Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament.
- -
Segona sessió: Hands-on! Instal·lació d’eines
- -
- 18 de maig
- -
- Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.
- -
Tercera sessió: Anàlisi i modelització de xarxes
- -
- 25 de maig
- -
- A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.
- -
Quarta sessió: Social Big Data i anàlisi de xarxes socials
- -
- 31 de maig (dilluns)
- -
- Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.
- -
Primera sessió: Introducció al pensament de xarxa
- 11 de maig
- Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament.
Segona sessió: Hands-on! Instal·lació d’eines
- 18 de maig
- Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.
Tercera sessió: Anàlisi i modelització de xarxes
- 25 de maig
- A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.
Quarta sessió: Social Big Data i anàlisi de xarxes socials
- 31 de maig (dilluns)
- Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.
Primera sessió: Introducció al pensament de xarxa
Segona sessió: Hands-on! Instal·lació d’eines
Tercera sessió: Anàlisi i modelització de xarxes
Quarta sessió: Social Big Data i anàlisi de xarxes socials
>> Places exhaurides! Apunta't a la llista d'espera <<
Una introducció neta i ràpida
L'anàlisi de xarxes socials és una eina teòrica i metodològica per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.
En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals. Acompanyarem el recorregut teòric i metodològic amb sessions pràctiques per a aprendre a dominar eines d'anàlisis i visualització de xarxes com Gephi i altres.
Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horaris i inscripcions
- Sessions presencials.
- Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.
- Inscriu-te en aquesta mateixa pàgina, o clicant aquí. Quedaràs inscrit als quatre dies que dura la formació!
Primera sessió: Introducció al pensament de xarxa
- 11 de maig
- Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament.
Segona sessió: Hands-on! Instal·lació d’eines
- 18 de maig
- Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.
Tercera sessió: Anàlisi i modelització de xarxes
- 25 de maig
- A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.
Quarta sessió: Social Big Data i anàlisi de xarxes socials
- 31 de maig (dilluns)
- Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.
-<p><strong>>> </strong><a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/2030/" target="_blank"><strong>Places exhaurides! Apunta't a la llista d'espera</strong></a><strong> <<</strong></p><p><img src="data:image/png;base64,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"></p><h5><strong>Una introducció neta i ràpida</strong></h5><p>L'anàlisi de xarxes socials és una <strong>eina teòrica i metodològica</strong> per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.</p><p>En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó <strong>com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals</strong>. Acompanyarem el <strong>recorregut teòric i metodològic</strong> amb <strong>sessions</strong> <strong>pràctiques</strong> per a aprendre a dominar eines d'anàlisis i visualització de xarxes com <em>Gephi</em> i altres. </p><p>Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img src="data:image/png;base64,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"><img 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"></p><h5><strong>Horaris i inscripcions</strong></h5><ul>-<li>Sessions presencials.</li>-<li>Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.</li>-<li>Inscriu-te en aquesta mateixa pàgina, o clicant <a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/1947/meetings/2184/registration/join" target="_blank">aquí</a>. Quedaràs inscrit als quatre dies que dura la formació!</li>-</ul><h5><strong>Primera sessió: Introducció al pensament de xarxa</strong></h5><ul>-<li>11 de maig</li>-<li>Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament. </li>-</ul><h5><strong>Segona sessió: <em>Hands-on</em>! Instal·lació d’eines</strong></h5><ul>-<li>18 de maig </li>-<li>Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.</li>-</ul><h5><strong>Tercera sessió: Anàlisi i modelització de xarxes</strong></h5><ul>-<li>25 de maig</li>-<li>A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.</li>-</ul><h5><strong>Quarta sessió: <em>Social Big Data </em>i anàlisi de xarxes socials</strong></h5><ul>-<li>31 de maig (dilluns)</li>-<li>Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.</li>-</ul>
- +<p><strong>>> </strong><a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/2030/" target="_blank"><strong>Places exhaurides! Apunta't a la llista d'espera</strong></a><strong> <<</strong></p><p><img src="data:image/png;base64,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"></p><h5><strong>Una introducció neta i ràpida</strong></h5><p>L'anàlisi de xarxes socials és una <strong>eina teòrica i metodològica</strong> per a comprendre les estructures socials, com emergeixen a partir dels comportaments individuals i alhora com els afecten. Gràcies a la profunda penetració de les plataformes socials en línia, avui dia podem observar i, sovint, analitzar enllaços a una escala que supera amb escreix el que era possible fa només unes dècades. Si bé això impulsa noves metodologies, les xarxes a gran escala que podem observar encara poden ser informades per preguntes clàssiques en la teoria de xarxes socials alhora que també estimulen la producció de nova teoria.</p><p>En aquest curs realitzem un recorregut ràpid per les idees i mètodes clàssics d'aquest paradigma. Demostrarem no com sols són aplicables a les xarxes digitals contemporànies, sinó <strong>com s'han desenvolupat gràcies a les dades que podem extreure de Twitter, Facebook i altres plataformes digitals</strong>. Acompanyarem el <strong>recorregut teòric i metodològic</strong> amb <strong>sessions</strong> <strong>pràctiques</strong> per a aprendre a dominar eines d'anàlisis i visualització de xarxes com <em>Gephi</em> i altres. </p><p>Curs a càrrec d'Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img src="data:image/png;base64,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"><img 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"></p><h5><strong>Horaris i inscripcions</strong></h5><ul><li>Sessions presencials.</li><li>Dimarts 11, 18, 25 i 31 de maig. De 18h a 20h.</li><li>Inscriu-te en aquesta mateixa pàgina, o clicant <a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/1947/meetings/2184/registration/join" target="_blank">aquí</a>. Quedaràs inscrit als quatre dies que dura la formació!</li></ul><h5><a href="https://diode.zone/videos/watch/4ca4d8be-479e-4c6f-9b53-bdcd0ee10078" target="_blank"><strong>Primera sessió: Introducció al pensament de xarxa</strong></a></h5><ul><li>11 de maig</li><li>Introduirem els elements teòrics i metodològics bàsics i veurem com aquests es relacionen amb les diferents escoles de pensament. </li></ul><h5><strong>Segona sessió: <em>Hands-on</em>! Instal·lació d’eines</strong></h5><ul><li>18 de maig </li><li>Sessió pràctica per a conèixer i instal·lar les diferents eines que ens permetran elaborar l’anàlisi i visualització de xarxes.</li></ul><h5><a href="https://diode.zone/videos/watch/86ce06ea-b24b-4841-8409-52ef70655b35" target="_blank"><strong>Tercera sessió: Anàlisi i modelització de xarxes</strong></a></h5><ul><li>25 de maig</li><li>A través d'alguns casos d'estudis aprofundirem en alguns dels elements apresos en la sessió anterior, com per exemple les mesures de centralitat i la detecció de comunitats.</li></ul><h5><a href="https://diode.zone/videos/watch/2deae660-72d8-421a-8da2-efdbc1904648" target="_blank"><strong>Quarta sessió: <em>Social Big Data </em>i anàlisi de xarxes socials</strong></a></h5><ul><li>31 de maig (dilluns)</li><li>Veurem com extreure dades de les plataformes socials en línia i com analitzar les xarxes d'interacció social que en aquestes es desenvolupen.</li></ul>
Description (Castellano)
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>> ¡Plazas agotadas! Apúntate a la lista de espera <<
Una introducción limpia y rápida
El análisis de redes sociales es una herramienta teórica y metodológica para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.
En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales. Acompañaremos el recorrido teórico y metodológico con sesiones prácticas para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras.
Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horarios e inscripciones
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- Sessiones presenciales.
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- Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.
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- Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!
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Primera sesión: Introducción al pensamiento de red.
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- Martes 11 de mayo, 18h
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- Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento.
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Segunda sesión: Hands-on! Instalación de herramientas
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- Martes 18 de mayo, 18h
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- Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.
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Tercera sesión: Análisis y modelización de redes.
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- Martes 25 de mayo, 18h
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- A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades.
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Cuarta sesión: Social Big Data y análisis de redes sociales.
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- Lunes 31 de mayo, 18h
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- Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .
- -
>> ¡Plazas agotadas! Apúntate a la lista de espera <<
Una introducción limpia y rápida
El análisis de redes sociales es una herramienta teórica y metodológica para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.
En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales. Acompañaremos el recorrido teórico y metodológico con sesiones prácticas para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras.
Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horarios e inscripciones
- Sessiones presenciales.
- Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.
- Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!
Primera sesión: Introducción al pensamiento de red.
- Martes 11 de mayo, 18h
- Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento.
Segunda sesión: Hands-on! Instalación de herramientas
- Martes 18 de mayo, 18h
- Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.
Tercera sesión: Análisis y modelización de redes.
- Martes 25 de mayo, 18h
- A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades.
Cuarta sesión: Social Big Data y análisis de redes sociales.
- Lunes 31 de mayo, 18h
- Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .
-<p><strong>>> ¡</strong><a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/2030/" target="_blank"><strong>Plazas agotadas! Apúntate a la lista de espera</strong></a><strong> <<</strong></p><p><img src="data:image/png;base64,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"></p><h5><strong>Una introducción limpia y rápida</strong></h5><p>El análisis de redes sociales es una <strong>herramienta teórica y metodológica</strong> para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.</p><p>En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino <strong>cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales</strong>. Acompañaremos <strong>el recorrido teórico y metodológico con sesiones prácticas</strong> para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras. </p><p>Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img 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"><img 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"></p><h5><strong>Horarios e inscripciones</strong></h5><ul>-<li>Sessiones presenciales.</li>-<li>Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.</li>-<li>Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!</li>-</ul><p><br></p><h6><strong>Primera sesión: Introducción al pensamiento de red. </strong></h6><ul>-<li>Martes 11 de mayo, 18h</li>-<li>Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento. </li>-</ul><p><br></p><h6><strong>Segunda sesión: <em>Hands-on</em>! Instalación de herramientas</strong></h6><ul>-<li>Martes 18 de mayo, 18h</li>-<li>Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.</li>-</ul><p><br></p><h6><strong>Tercera sesión: Análisis y modelización de redes.</strong></h6><ul>-<li>Martes 25 de mayo, 18h</li>-<li>A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades. </li>-</ul><p><br></p><h6><strong>Cuarta sesión: Social Big Data y análisis de redes sociales. </strong></h6><ul>-<li>Lunes 31 de mayo, 18h</li>-<li>Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .</li>-</ul>- +<p><strong>>> ¡</strong><a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/2030/" target="_blank"><strong>Plazas agotadas! Apúntate a la lista de espera</strong></a><strong> <<</strong></p><p><img src="data:image/png;base64,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"></p><h5><strong>Una introducción limpia y rápida</strong></h5><p>El análisis de redes sociales es una <strong>herramienta teórica y metodológica</strong> para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.</p><p>En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino <strong>cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales</strong>. Acompañaremos <strong>el recorrido teórico y metodológico con sesiones prácticas</strong> para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras. </p><p>Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img 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"><img 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cnIyffLJJ7z4x7Vr12jdunVUt25dmjFjBi/kERwcTPn5+dSyZUsCQI8//jhdunSJJEmiyMhIcjqd5OXlRXPnzqWioiKaPXs2NW/enJ577jk6fPgwASBvb2/Ky8vjGlZycjJ99dVXBIAyMzOpT58+BIB69uxJZrOZfvrpJ5o1axYBoM6dO1OvXr0oKiqKiIhatGhBZrOZrl+/TomJiWQ0Gum1114jIqI6depQ79696caNG2SxWMhqtVJOTg79+c9/JkmS6IMPPqDLly9Tw4YNqVGjRrx4iq+vL40YMYIAUJs2bSg/P5+8vb2pTp06dO3aNfr0008pISGBbDYb/fLLL/TWW2/xIiuJiYnlCrNAZBz97TA7wfLlyzFjxgyuVS1duhSbN29Gz549y4XhuNuizcrKQmxsLA4dOsTfe+yxx7B27VoehuHOQCk0rZqpaWnvbXFxMQoKCrBv3z7k5eUhNTWVe5trs9kCQJ8+fZCTk4NRo0bB398f+/btg9FoRGZmJq5cuYIlS5YAAP75z3/ihx9+4DYf5jwMAFeuXEGjRo0AAP/+97+RlJSElStXYtmyZfDz80NMTAy++eYbyLKMlJQUqKqKiIgI5ObmAviPV/7169eRlpYGp9OJNWvWYM6cOYiOjobdbue2tJKSEmRlZXGv/ZycHJw8eRIXL16ELMu4cuUK/Pz8kJ+fjy1btqBjx47o1q0bnE4n6tSpg6tXr6KoqAibN2/G5s2bAQDPP/88NmzYgFmzZiE6Oppn4XXN9yaM7r/BuMp26s6fP49Zs2ZBVVWYTCYeAvHss8/ymLGKrskE3JIlS3D48GGeF8toNOLjjz9GUlJSOf8Yd7sp2uWIEF41Y6xo00yzrA0GgwEWi0WXjYMZvrUVxDdv3owPP/wQffr0QX5+Pnx9fVFaWsrL2JvNZphMJqiqynf+tGYF1o5HH30Ub775JsaPH4/9+/fDZrNBlmVeHIUJSnZd1naj0cgL9LIYRW0OLya02GTKhC67DvupKAqsViu2bNmCDh064Oeff+aGfNY3zKhvNBqxdetWXLlyBQsWLICqqjhy5AhXDGryDmKNEVhV6aATJ06grKyMx/mxkuPnzp1Denp6pbMCGyQ//vgjLy3PvNYNBgMOHDhwW0JU5FmvGWOGhU6xh1OWZZSVlXEXByYwFEWBp6cn3/Xdu3cvevbsCV9fX+Tm5iI4OBgAUFpaCrPZzIVTSkoKhg4dyitJ+/r66uIT2e5x9+7dsXjxYrRu3RpRUVGw2+24ePEiJk6cyNvl5+eH4uJiWCwWXZwhE1YPPfQQ8vPzkZqaym20drudCxr2XVrXG4fDAavViuzsbHTq1Anx8fFYsWIF7HY76tati6KiIj7Bl5WV8doCiqJg/vz5mDNnDsrKypCXl8eFak2eiGvMkrAqGktFebnd5U+v7BpsAGmFz622edl3shmoIm9lwd1BURTExsYiIiICZWVl6NGjBxcCLVq0QEBAALp06YKSkhIMHz4cycnJ+Oyzz7BixQq8//77WLt2LTp16oTPPvsMhw4dwunTp7F27Vp06NCBa0zbtm3jGWm/+eYb7Nu3D1euXEF8fDwuXbqEhg0bwtfXFy1atMD48eMRGRkJLy8vrFixAufPn8czzzyDVatW4eOPP0ZGRgZWrVqF9u3bIzc3F0OGDMH58+dRUlKC0aNH49KlS/if//kfjBkzBrIsY8CAASgsLMTw4cORnZ0NRVHQvn17HDp0CI0aNUK9evUQHR2NsLAwlJSUID4+Hu+99x6OHDmC9957D6dPn8bly5cxbtw4nDx5Eg6HA3379sXhw4dx7tw5mM1mfPzxx5gyZQoOHjyoG+NiIr6F0X3ixInlyntpYcZ4Vp6LGQbZdrIsy3T69OlyXuruaNOmjc5oztrAiqFWdD4rQcaqS7vbYhavu/sKDw+nwMBACgoKopCQEAoICKCAgAAKDQ0lX19fCgsLI39/fwoPD+ce661ataLAwEB+jejoaGrbti0vFVa/fn3y9/enBg0acPcEk8lELVu2pHr16pHNZqOgoCCy2WwUEhJCwcHBZLPZyMvLi9q2bUtNmzbVuRN4e3tT+/btuZtBeHg42Ww2atCgAQGgQ4cO0bx58+jBBx8kPz8/7vYQFhZGfn5+FBYWxv+3kJAQCgoKouDgYAoKCiJ/f3/+s0GDBmQ0GsnLy4vat29PkiSRzWaj6Oho3sb69euTr68vb1tISAgtXrzYrTtDZWO71lZ+ZoKnY8eOle70Mf+s5557rlxdOXaNTZs2cf8qd6E2iqJQRkYG+fj46PxtmOBq165dkU8LNwAADXpJREFUpW0oKysjIqK5c+dWWGdQvO6Neoa4jZqQv2U3WBvWU9m1AgICKC8vj958800eDnQ3dp8lSaLp06fT0aNHqVu3brf9f9ZagQVNQdKvv/6aiIjsdjspisLdDFj5+hs3blBwcDCP02Ivk8lEkiRRr169uMBhGpnrNRYsWMCd8GRZJpPJxGPHJEmiXbt2uW0DE1bZ2dkUEREhysbXYCGkvTfu/tYWuGXaubt7qT2WxRxWdH02cbKHXns9JoS0xX3Z7z169KDnn3+epk2bRm3atNEJUXf/g/Zc18+152l/r+i8Z555hrp37/6bxrIQWP+tvsuWda5+VDdv3qQhQ4aU8xNxreQ7b968CpeCycnJ5OnpqfNChksQbf369Sk1NdXt+cXFxfTII48IH6wa9OrevTuNGDGCEhISqHPnzrrxwQKfteOETU6uvkZs0uvduzedPXuWAgMDywkdV+GmvY7rsVphqBVkQPVVla6sSjnuoeDnahdY2pscGBhIf/vb3+jMmTOUl5dH6enptHHjRmrVqpXu+M6dO9MTTzxBY8eOpZiYGN1nw4YNo6+++ooyMzMpJyeHjhw5QjNmzCCz2cy/Jzg4mOLi4uipp56iQYMGkY+PDz/f39+fli5dSmfOnKHc3FxKT0+nTZs2Udu2bcvNfEJoVN/LYDBQ8+bN6cKFC3Tq1Clq1qzZbWUtAEB+fn5c6wZAjRo1ohdffJGPh6effpoLwlst/+bPn89DfNik6JoZwZ1GqBWsv1XoVFX7dJ2gtdqmEFi3IbBctRYPDw8KCQnhsVWsU5s1a0b79u0rZ1vauHEj+fv7l7MPhISE8FmQXX/27NmUlZWlM+5fvHiRxowZozvfarXq2iA0q5rp6Z6UlEQbN27kE96AAQMoKCiIHn30UVq4cCGFhYVxwfDII4/QM888Q08++SR5eHjQCy+8wD3Xo6KiKDAwkDp37kxms5nCw8MpPT2d3nvvPerevTtFRERQbGwsNWrUiABQ3759qW/fvnwCLS0tpdmzZ3ObUGhoKE2cOJEmT56si1X8PQKposnybk+etTqWUFscgjnjlZSU4OrVq8jPz+eOcU2aNMGXX36JHj168NQvzL/kscceQ3JyMry9vXmSvaysLFy9ehWKovC0MK+88greeOMN+Pv7cz8bRVEQGRmJdevWYfTo0QAAi8UCu93O26B1vKvpfiq1CYPBAA8PD1itVhgMBvj7+2Pbtm1Yvnw5GjZsiISEBHz44YcgIrz44os8Zq5bt26w2WwwmUwoKSnhbjGDBw/GgQMHEBoaCoPBwP2gJEmC1WrFJ598gri4OABAXFwcNmzYAACwWq0oKyvjBVcjIiKwfft2HDt2DNevX0dSUhLMZnOV/Mqq8rm77AyCu6hhVTRjsJkRAG3cuJGIiBvQtUZ1u91ORESzZ8/WuRxoz2/ZsiXP8sBcF5iGxYz0WVlZfMvb1UgpXjXDsI5KYgkB0OnTp/kyb9CgQZSdnU2SJNG2bdvo008/JYvFwq87dOhQOn/+PP+7YcOGlJeXRw888AABoLS0NBo7diy/9pEjR+j5558nADRixAg6d+4cd124ceMGj1N89dVX6fTp0xQaGkpdunQhRVH4kvV+0NJrfbYGd2lpmRajKAqCgoLQv39/7tnMjmMvVnY8ISGBO3Qyj2B2nREjRuhyHmk1JXa+v78/+vbty48RxSZq1tjQ3jNtyI02XMpoNCIrK4uHr7DIiJdffhktWrTA9evX8dZbbwEAvLy8ePgMAK6da0N96tSpw8Ni2NiTZRmenp68VJyHhwdkWUa9evVgNBrRpEkT5Obmol27dggLC8O4ceNw7dq129KG7nTo1/2yKqhRnu5aAaEVNiEhIbwqs7uOZ/FWISEh8PT0xM2bN8t5xIeHh98yTzURISwsrNL2CWqGAHMt88U+Y8syRVHgcDj4ku7IkSNo1KgRX8olJSWhsLCQh7cQEV/WlZWV8eiHwsJCfn2j0cjLfLHwFm178vPz4XQ6kZWVhaioKOzYseM3C4s7PdZulb/+njED1NSZVHujCwoKUFpaWmknExHy8vJgt9vdfs4CSm81o+Xl5QlhVQNxd+/8/PzQtGlTREVFwcfHBz4+PggLC0NISAhkWUZ4eDjq168PT09PzJ8/H61atcKmTZuQmpqKoqIiZGdno2HDhujXrx8iIiIQHh4OPz8/RERE8HE3bNgwtGzZEhaLBRkZGRg3bhweffRRDBw4EP7+/mjcuDFKSkpgMpkwbNgwNGvWDGvWrEHLli0xb948tGrVCnFxcTwlck3ReO7V8Szf7S9ctGjRIgB46aWXqtSpkiShoKAAQ4cORVhYGM/coIUFdG7btg3Jyck8GJap76qqYubMmWjQoIHb1LHa1LZz5szBjRs3Kr2pwuhe/aiqij59+qCkpATnz5+H3W6Hv78/8vLykJmZifT0dDzwwAM4ffo0SktLce3aNfTp0wctWrRAcnIy9u7di6ysLCiKgr59+yItLQ0NGjTA+fPnAQAnT57EoUOH0K1bN3h7e+OHH35ASkoKWrduDbPZjH/84x+QZRl2ux1Hjx7FmTNn0LdvXxQUFOCzzz7D3r17ERsbi9atW+PUqVM4deqU26o89yL/fYTx3+f4pft6oFXk1gCN6wCrQiLLMjeSxsbG6kJ1FEWhsrIy7oWel5dH0dHRZDAYdB7sAGjAgAFuQ26YVzwL51m+fDn3o9Few52zqnjdm8b6O3Ef/8hrC6P7PSSwKtpBYQLjqaeeops3b5YTPJcvX6bevXu7PXfIkCGUmZmp21V0J7w++OADXa5td20Tu4Y1r8QXC9GCpryWq7c7C01h91AbuM7CbtjnFZUBg5uyYNpj3ZUHu5vxgbVFYFVrIVV3diKW04dlZvzqq6/wySef8OOio6MxcuRIxMTEwOFw4ODBg1i/fj2uX78OALDZbBg5ciTat2+Ppk2bolOnTuVyCBkMBuTk5ODf//43zp49ix07duDLL7/kbejRoweGDx+O0NBQ5Obm4osvvsC2bdvK2bOEbevubsjcT3bFe7n9tbaQKlxirhITE93G8X3++ecUFBR0yxCG7t2704ULFyqtnENEtHbt2nLVRths/M4777htw86dO8lmswkt6z5bGoq2iSVhlQWW1rHz5Zdf5vYpVn6LlfIiIvr66691SwD2O4sRjIqKouzsbB6uo3UQ1aaNYUUDtEKKlTlatmxZpW3YvXu322BW8aqZD//dqLRcG1+1UmBpNZWGDRtSUVHRLXNRxcfHV5gPa9WqVTw1TGVVbwYPHqwLUGXCJzo6mkpLS8sJOtc2xMbGVrm6iHjVbk1ECKw7j6EaBRdP4t+lSxd4enpW6kZARBgwYIBuDc1iwCwWC3r27KnzhHf9Lubod/bsWV62XEvPnj25x7O7dMmsDf379xeuDdVoMxF9X7sx1ISBeKsCjsypk3m7u+Lh4QFvb+8qhTOwIgTakvQAULdu3XIFWd21qW7durc0pgruvgAT/V47qNbQHCYgLl++zMNrKhqgRISMjAy3QuHmzZvIzs5GYGBghUKDiJCfn4+srCzdoGc/L1++XO77tXGN7Gd6enq5Y25VwELw+9CW6NIKKxbv525iYbvCYhf3zkwOtTau1tXoLkkS+fn5UUZGhi5/uzYjA3uP5RrS2o/Y7wsXLixnw2K7gsz+tG7dunI2MNaGoKAgysrKIlVVy9mwFEXhNrD27dvfMupe2FzE635+aXfe7/rkVR0CS6sFMdvSqFGjeH4hbc0/Vk/wnXfeweTJk3V5qbTX8fX1xb59+9CyZUteo44tFQwGA65du4auXbvi4sWLusKq2jY88cQTWL16NQDwEl5aW9v//d//YebMmbrCnE2bNsWCBQv48WJpcudneEmSUFpaitmzZ/OwKQ8PDyxZsoTnNmNarrtq3YLf1u+qqsJsNuOf//wn9uzZo9N0Nc9f7fLD0mpJo0ePpkuXLum0m4KCAnr55Zfd5uKGi4d8eHg4JScnl/O/+v7776lFixaVakbs/fHjx9Ovv/6qa0N+fj4tXLhQ54bBfg4cOJAEdweW7RMA+fj43LKkm+C3o31+Zs2apVuZCE/3/2pSqqrCz88PPXv2REREBHJycnDgwAFcuHChnF3JnZ2Dvd+pUye0bNkSRqMRp06dwv79+3nQtKth3dVwq6oqAgIC0KNHD4SEhCAnJwfff/89Ll265Pb7wsPDERcXxzU6YTP5YzQsh8OBjz76CIWFhQD+k7tq1KhR8PHx4X0vuLM2Q6Zh7d27F8ePHy+XAqo6NKxqF1jah5wtzcrtDGjKdFcktLTXcyeUKhNWWiprA3OFEILp7j48oq9r1n2o1QLLnZaj/ey3lINndi9t1tLbGfSu57PZpqIb6W63Sjxod07DYpON61hgGT8FfyyuPotCYFXDbOHa8QKBoOqTSHUJLGNN+KcFAoGgSqsf0QVCAAsEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEgnuN/wdV4CfEEDzB/QAAAABJRU5ErkJggg=="></p><h5><strong>Horarios e inscripciones</strong></h5><ul><li>Sessiones presenciales.</li><li>Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.</li><li>Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!</li></ul><p><br></p><h6><a href="https://diode.zone/videos/watch/4ca4d8be-479e-4c6f-9b53-bdcd0ee10078" target="_blank"><strong>Primera sesión: Introducción al pensamiento de red. </strong></a></h6><ul><li>Martes 11 de mayo, 18h</li><li>Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento. </li></ul><p><br></p><h6><strong>Segunda sesión: <em>Hands-on</em>! Instalación de herramientas</strong></h6><ul><li>Martes 18 de mayo, 18h</li><li>Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.</li></ul><p><br></p><h6><a href="https://diode.zone/videos/watch/86ce06ea-b24b-4841-8409-52ef70655b35" target="_blank"><strong>Tercera sesión: Análisis y modelización de redes.</strong></a></h6><ul><li>Martes 25 de mayo, 18h</li><li>A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades. </li></ul><p><br></p><h6><a href="https://diode.zone/videos/watch/2deae660-72d8-421a-8da2-efdbc1904648" target="_blank"><strong>Cuarta sesión: Social Big Data y análisis de redes sociales. </strong></a></h6><ul><li>Lunes 31 de mayo, 18h</li><li>Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .</li></ul>
-
-
>> ¡Plazas agotadas! Apúntate a la lista de espera <<
Una introducción limpia y rápida
El análisis de redes sociales es una herramienta teórica y metodológica para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.
En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales. Acompañaremos el recorrido teórico y metodológico con sesiones prácticas para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras.
Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horarios e inscripciones
- -
- Sessiones presenciales.
- -
- Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.
- -
- Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!
- -
Primera sesión: Introducción al pensamiento de red.
- -
- Martes 11 de mayo, 18h
- -
- Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento.
- -
Segunda sesión: Hands-on! Instalación de herramientas
- -
- Martes 18 de mayo, 18h
- -
- Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.
- -
Tercera sesión: Análisis y modelización de redes.
- -
- Martes 25 de mayo, 18h
- -
- A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades.
- -
Cuarta sesión: Social Big Data y análisis de redes sociales.
- -
- Lunes 31 de mayo, 18h
- -
- Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .
- -
Primera sesión: Introducción al pensamiento de red.
- Martes 11 de mayo, 18h
- Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento.
Segunda sesión: Hands-on! Instalación de herramientas
- Martes 18 de mayo, 18h
- Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.
Tercera sesión: Análisis y modelización de redes.
- Martes 25 de mayo, 18h
- A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades.
Cuarta sesión: Social Big Data y análisis de redes sociales.
- Lunes 31 de mayo, 18h
- Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .
Primera sesión: Introducción al pensamiento de red.
Segunda sesión: Hands-on! Instalación de herramientas
Tercera sesión: Análisis y modelización de redes.
Cuarta sesión: Social Big Data y análisis de redes sociales.
>> ¡Plazas agotadas! Apúntate a la lista de espera <<
Una introducción limpia y rápida
El análisis de redes sociales es una herramienta teórica y metodológica para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.
En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales. Acompañaremos el recorrido teórico y metodológico con sesiones prácticas para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras.
Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).
Horarios e inscripciones
- Sessiones presenciales.
- Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.
- Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!
Primera sesión: Introducción al pensamiento de red.
- Martes 11 de mayo, 18h
- Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento.
Segunda sesión: Hands-on! Instalación de herramientas
- Martes 18 de mayo, 18h
- Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.
Tercera sesión: Análisis y modelización de redes.
- Martes 25 de mayo, 18h
- A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades.
Cuarta sesión: Social Big Data y análisis de redes sociales.
- Lunes 31 de mayo, 18h
- Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .
-<p><strong>>> ¡</strong><a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/2030/" target="_blank"><strong>Plazas agotadas! Apúntate a la lista de espera</strong></a><strong> <<</strong></p><p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDgAAAQ4CAYAAADsEGyPAAAAAXNSR0IArs4c6QAAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJREFUeJzsvdl667gSpbkCADXZ+2Sdi+oX7Ffup6i6qsptDSSA6IvARGqwLUuyhvjzy22NFEWRGBYiVtDw//2/jLuAfnsHvgDfdjcZOPmBd3PIPj+FCAQGyyEkuT0MA8CAcw7GGDBzee6zrZ3Lue/k8t66fwTCMAzYbDbohx7OdVjM55jNZjDGjN7NaQM02pai/JR8RtWbDLnGQmD87//9v/Dn/R1//rwjhAii5vU3PAsHP8APA5bLJUDUNBmf7cuZbe6JJoRe9OLLrZZyiNsOg9r+EARwZAx+AIFgnYW1Nl3LXF979mfVfxXlOpx3ft7J5OOKPPt199N26fm4+Tn92Tzxbrj91e5u/onKa0MyaGNmxBhhrR2JAT8ZyF0LEV5MGpAyyBBiiNjutthsNmAw5rM5lsslnHPy/cB16tnMP5HvP0J7pNw9rVxRHiMCQBiGHjEy5otFOoeB3xA3ABQBM1/zpU++v8tdUW5HugZ88AAg4oaxYOaD/YR2G8ozoV2AoijXQgUO5SaMhAsGYoggkIgbFw5ruPQg0BiLGKNEYBBh6Aes1xtsd1vMZh1WyzfM53P5GpzWSanuC1PdJy7/6lD1JTkRqPBjypyIwMzY7bboOoeuc4gxgn5xNGnIgIxBCEFWp698DbxqlIZy/+Trk9JJ2vseMUZ0roM1tr4uRTPmKA5FuV9uH1V7jHtcJFMU5faowKHcHGZGiAHGGhgyJRRXnsTdzf1jjOkWYbfb4d+//4Ij4/3tDcvlUlakmRE5ykSOCCEEGDMODq7RHHf2BZWH5fBQjhFDRD/0mM9m6WxjEFIU0i+cf0QEawxijJN91jU85bXglHbFYAQfaiSjNTo5UxRFUZQLoAKHclUO5Q/nFBVjRAyoUQ8/yzX+Knsh/c3jNHkNpf3yIWD98YHtdgvnHN7+84ZFCv3PAohJaSwcGcZQFTQmG6XfXEpXfpcr/exyHYlDDACEGBBDRPfWlaiO/R25ndBBBBhrk+/OoSvtBKde8oJaobYcl+bGR5RRRPBhGOCcQ+e6kbeTvGx8X1EU5Vk4p9XVlvAY5/Zhz31EbytwXDM8+xl5lnOvMRYFQ6IbUqRDXs2Sl33dUPAnZqHt3K7GWPD+NlPI/263xcd6jRgClssllqsVrDUl7L+Gw6dQ4rLtyfcZmXAoypTz3DSrSEjl/77vQcbAuQ7MaELi82fctnFhFh8OAhBihEtRTz/dj2dpIr/Ps3/z+28jT5l3tikotcMRs+ksbng/wFqLrnPlZW1qlQobivJ9zr9ujrc557dGt27Hbt1m3PrzHqVf+D5nf7Obf+C53H5HNYJDuSrcTMCICJEjmBnW2JsP4FhGl7WIA2d/jBq430aTDN5ju91is91i1s3wtlqVCinfEWMU5dqIgCDXGccI7z3cEQNfKnEeN93DMuHjyID95OWK8sC0kYlyXQKA9D0xBoTgQWTgnEMR2NU4RlEURVEuggocynWZLNLGGAEGjDU3Fwj2BpA5ciSX6kvVUiJHbDcbbDZbxBjx/vaGrpuVlbapkeiYOzQRUZ6aUYUUAkKIGLzHcj4flV/+7cUPQwQypoicivLsEKX+JfULzBHeBzADzmUBcpqypf2HoiiKovwEFTiUq5NXjou5aPLeuPl+cI7UoOKDwU0ohzEGw+Dx8fGBfhgw6zq8vb1jNuuKKVwef57eex2kKrcir/6KcGCMQfABHCNc16XX8MjL8zekBYYIHIaS0WgSExXlOdlPURFxwwNgOOdKNSEepaZov6Eo94a6pinK46ECh3JdmhKwMaToDWcOmo9efVfIlOD8kViRKrl8rNdYr9cAA8vFAovFEs45MMeaH21kSa5NZTmMdodPzZ397EQAopyXgx9gjE3h7/dxHuZmwFiDGAMiM2xevP7svTrnU67M5dMl9yMyfKmYksUNFE+OsWeHoii35/T1d7zLv48+VlGUMSpwKNcnzW5CCCBD1cPi5nD13gBLiVrIfv3771/sdlt0rsP7n3d0sxmQQv85m4cCOd5YxJISfpw2qii/AonhrTGSnjIM6JyDNTKJkmClti7Q75ArJwEAxwikFezf3i/lNfgd0075zBA8YoxJeMziRhU1VNtQFEVRlMtxW4HjbuaAty8Ld5InH9wQCIEDIkc4e91T7tChLmtpabVMUkxk1Wy32+FjvYb3A1bLFZbLVRmAgtLEsakAk98LMELMIsmhT9U0lafmbtoymRyFwHDOYAgBIQR0i1kx020ND3/OD/zk84Tu4P7c0QE9g8fee+XntGdADWin1Id4PzYVrb458l4i8crRSA5FUZTDaDHUS/PcR/TGERx3dFBuvit39N2PcoVhOgMwQPABBJLQ3C8t2p5abzskHjQRFtRURMmVGwCZWKUPDyFgs95gu9vCGov//o//wjlXo0vyW0vJ133jDUOnMjMf4fdWrsMNf/sUYWSsQYiMECNCiOiS/wYhVS255D6dsSkCgVNZZWsMQgjp/rOsYD/8F/iE+5Fw7q986tRHo4rpMUb0/QAA6LruSDoKfSHlUVGUe+Q5ytI+SrvzMDVRv42Wlz3G+TuqKSrKdSGpnFKiN0o91s/eeGpSdujxOkBkjAeLebFYSvRJ1MZmswEzY7lYYrFYwFpbB5dlsKrWUsqdk2ZWedLU9z2sNXD2vuqw1nLR7XWpq9XKY3NYmKil0ft+ADNjNps1Yp6e84qiKIpyTVTgUK4KgeCDB5BKw9YnrgJzTJEaKNpETksZ+gGbzQb90MNai9Vihdl81lRVaQ2jqtjxMOK28qLU0PZd36Nzbnyt3QONmWL24RBPAvME0RvKa8KNEC73BTnP24op+ZxXFEVRFOX6qMChXBUGI/hQIyRuEBBByf2TSdJIQojY7rZYrzcAGPP5HMvFsuxTzn0u6Sgke64odw9J+UljDLwPCN7jbbXCyeypX6EqhUQEY2S/FeWxqdFIQBXxvBdTUedcqsTVvkdVc0VRjnN33beiPCAqcChXJcYIgEtZvMuN6w53AVKGUqqeGCIMw4D1eo3tdgvXOayWb1gsFrJvzKlSynSneJTWoij3DDNgDGG328EYU/w37msiVfeDUmnmEALuax+Ve+H+fDaOM01TCSGU6CRjbHq+Tcv6rT1VFOV+0LK0inJNVOBQrkrwAcbYWh7yy43zV0aB+yJHDBEAgQnYbbf4+/cDIQQsl0us3t7grC0RG9aIi6iU2Kw1X7ndvGrpyj3D1dtit9vB2rpifE/zqBwVlSd3JhuNsoqIyqOybw4aY0CMAUQE52qE4Pgc1xNeURRFUa6JChzK9WCJ4Oi6bixsfGHRdj+rub3dGIlOXpNXhnPUhnUO/7z/g9lMHOzbqA25GWpKS7OhtoSfotwrUgbWIMaIYRiwXC7TZCuVNd672H7rfK5GqNVclMvqdhY6flvw0KtdOczxAuRE4uEUQoD3Itp1nSuihzGmpGOVa1NFDkVRlJvw7LVllMNcXuA4Hlf1ujz5VUJ5JYtqDjIA+OBBhiQ6AhKea8iAicsY72QocprxVK1hWosP4EYJYUiFlPV6gxADFosFlsslrLPlcyhvN72pTU/RleQn5Zzf9UHaK4JcI8PgMQwD/vvf/wLA6QiOH57n5719eq1RESMlfS0CMJgaNcprf7S73+TZG4HbntiPlGpynHEZ2Hqbm8e4pKZ0XTcyFW1TVIRnOCaKovwG57app6Onbz3guXUb+DA1Ub/N836zn0IawaFciDzQi5wiJSJijLCmll8dNcyfXJUxMoyZvqxWOiEQQPUSHbzHZrPBdrdD5zr8eX/HbDaT1TOkQSZR2YKiPAMSwUHY7bZwziWxIBvI4I5O9f2Va6JsNLq/os18NzuuvDStyEbN7XGZ477vk7iRK6ZolIaiKIqi/BYqcCg/hlM4PBEhIspfHwEW088S1TEZ751Sok3yx5CJjgwW20FmKQfLjN12i+12hxAi3lYrdN0M3ayTKdUoR1onTcrzwczYbDeYzeZjgePOyCvfORXFGINhyD4ccn3m5xTlvpgmTVbfDe89QgjoOgdr3d7rFEVRFEW5LSpwKD+n9a5gER2ycee5pWFlktPmLbdiB8NYAz8EfHx8YLfboetm+PPnDV0nXht5OFpEkZwWoyhPBBFh6AcEH/Dnfd48c6/ne17Z5rLSHWOEtfbX/TeUy/I8P2UVN6bnqPhueDhn4ZxUL2ojOxRFURRFuT0qcCiXI63EhhDAYLi0mlX8OaZh6DnV5ABElOM2RtsACIYM1h8f+PhYgwEsV0ssFktYa8dby16LHL/m+aEoD8h2t4UxBvP5/I5TO1KFomaCuO/DUaM3Wr8D5fd57XazppvIOSvnZYwBwzCAiNB1M9R0FjUSVRTlvjjVhmvpWeUZUYFD+TlUjUazwEEkQkRLrXzylYGfVDspwSE5HDgE/F1vsNluYMjgz/s7Fot5Gk42lVCyAylJlQkZlGojrjwXzIxd36PruhQFwaDGm+Zexi05DaW9BokIxkgFmH3yhPJOvoCiJDhVB/M+JHEjVeiKv2GKqyiKoijKlMsLHDcdj+rg9x5oBQtmKf1oWofQaZXKcSGUybbaO7XySYwRfd9js9mg73sslyusVitYZ0fqc1n5Tf9kU9EYD0eRKC/AUzUT4wsohojgfSoPmyMeKEVHHX7Pl47HxS+Tdh/qDhhjEILHMf+Nc9NWnuonV36ZHEUo52KMUaIUmZPvhoh0beljTVFRFEV5bLS87DEe48hcSOD4xZ/zrs6ke9mZnwzvG0ONE4w1i2QyasQPAIQSci6vPVw9ZVw9b/y5eZDIEBO3zWaLXd/DGIN//vkH3UxWrFOgx94kSKqsNPd1wKl8mzs7Z6Y+uQQMfkCMEcvlcvwcA0zNRIvbjYweOPpRl6RUUhpd/wxrLfoeCCEeMEj9STt2Z7/dUW5dtvX8Z18Z6Y/ktpSDDXDOwRhbopME0ggORVEeiuuUnj39zvPQ8rKX4uxv9kCHRFNUlAnnLZeSIXBkRI4wZL4uKBRxoo0CQaqQAux2O2w2G4QYsZjPMV8s4KwVA1MAsYZpKMpzM+0gGBj6oZSHTS4XjeB3bxfGONKq+m1Q8eFoH9eMMuX3ydV9asUUMcUVcUNRFEVRlPtDBQ7lLA5ZqIUoYbvGmUNv2cMYyVvmNpSDRCzxPmCzXmO328Fai7fVCvP5vFReALPUWGnCgb8We6Ioj8h4tp9FgX7oMZvNYAyBOe75XHxlW79FFjOsNSXkvxo03sc+KkruUUIICMGDyCQxTnsaRVEURblHVOBQziZn+ROJSBFDgCGTRIjPyXnLnGKesuHgdrfDx8cHYghYLBZYrlZwKXy9VmTJRVXqIHMawa8oz8Hhs9r7gBACVqtVMTkkSsLBXoWi+7wyiABjLLz3ReDQeaNyb4QgFVOMMeg6p+KGoiiKotwxKnAoDdNB26lJ0fi1MQREZjhnpaLKFyqmEJmy2kzGJK+NDT7Wazjr8Pb2jtVqWYzdjuU3t5EbOux8UY798Pc5rz8fUS/AzE2Jym7kEQCk0IhDx6RRAW85RztlFmqtAUCIMX5ZHFXORVvI7yIVUzyIkHw3TEqf0lKwiqK8Llp6VrlnVOBQTvC1mAgGEGIEQaIw2oZtfyW5eV+TXrLb7fDx9wMheMznc7y/v6NzDsyMECLISNlZ5lgqpEx3MZvf6JBTeSoaY4qSwJEEDmttqeIgE68sdOy57v565gcfFFboSLlYjcdSfh9mqZgCAM51E3FDURRFUZR7RAUOZQxP7hxdGa9LshwZHCPIGJhU+SSTxY281jWKtiCCDwHbzQbb3Q5EhLf3Nyl5CSolZ8VQVFZ4S6lXarZEDF1Qe3Gecr4xUe7KtcRSqjKGEi7PzOn6YoAfKc1DLlqZOMZGoOHRKxTlutTOo1TwSuJ6jGKA65xt/G1UgFMURVH2eZQaMbfntkfmuMBxMuT7+X+G7/MMx0QGeZSEjbLiirF20JoYEkSkAAid6+qrR6Xz6nsJst0IRr/rsdls4P2A+XyO5WqFzjm0S83tRK1ub7+U5F0WjVDuhAc+MdIFw4CIh+nay9UcZt0cQI5u2o+WouZfTISDW/KZ4CLlYkOaTLraxjyMUnPrcq+Pclwei7aKT4wSvWGMTedkedXkr6IoitJynfSVR5EOHqiW6jd5pPKyGsGhTKhRG9O5RY6SkCquKaqCo4THW1NKxeZKKMx5NZlGqSreD9hs1tjuenRdhz9//qRKECm9JakpOnxUXpemVU8BS8WvBoRh8DCpmsNZ27wzsngZI+NbX0lRLkIVD3OFIu89jDHJd0PKlkvgotbrUhRFUZR7RgUOpZCjIEqOcVY4RNEoz1PKm48phJchlRAIhFiEjFxhpYobkaNEbWw38N5juVhgPp+j67ryOnnPob3T/BPlxaDx7RyhwRj7b+wJF3T0zp3CMCYJoU2ayqfVbhXlIvAoOqOaitKkHKwKG4qiKIryCKjAoRTGEbgGdSDHiIFTdIUIH9nQkJlhjYUhg5gmYIQUyQEAhmBApULKZruBNRZ/3iVqIw8ex+aIx2Y2KnIoL0hjakhECJ4RUgnlWnXkkF/OI10r1Wi0NR9WlGuT7aRyqWXvPQDAOVuuL+nrfnMvFUVRFEX5KipwKAWOLEahxqSBXsAwDBiGAT4EcJl8GDhnZXXLEFZpopWrIHAqUZl9Aba7Hf5+/IX3AavlEqu3tzRwTNEe2baDGUCqpEBHa6/c5Fgoyq9yUKyQc38YBjCQDEbr9fbpJu4UZsAYjASOsvcvrGmqz8ZtyMI6M8P7ATGGVDGlzZV6JONeRVGU++Xcvk1LzyrfQQUOpWCsBUAYhoDtdovNdot+JwIHkBeSuZR1NNZIicoQsFwuYa0dVVEZBp+8NrYgIvznP38wny/k/Wm1TMwF0KSnECgJI6if2vDCMx7lBannezboHYYBhgiu6+rVMbksHu0KEZEjr5ZHMJtP3qEolyJXTBHjXuc6OGexX4u8TVV5tCtMURRFUV6H4wKHCmVPyqFKJPI4M7Dre6zXG2zWG/TDgFKhARiVhg0xwocAMiRRHt7jbfWG+XwO5oi+77HZbOF9j9lsjtVyCedSSUuglHwte9JUXanlYBXlVTjU4DZljFA9KYr/hjngv4FHvW44iZukRqPKFTksTkjFlAhjLFznSkRhTaEEQPtVihRFURTlEjxKjZiz+OzLnfwS5x2ZExEcD3HIrsS9fPdLq0wHyqIU3wvCbufxf//9i816g5jCxLOZqLydmr2qIbshMP79d42hD3h/fwdzxG63BRnC+/sfzGYdrLHg9B+BRiWDqNn25Obn30F5IZ78tz9ampsBMgDLRGwYBvzzz3+KEefx6+WeVOrjv101FOVRqhs10WC347xPPLcknk6Yb0etQUQjzyepmBJAxkhUVOnuqHSRY08Y/c0URVFuzen+8ng/e35qi5aXvRVHh78/OCSaovKKcK1ukiukDN7j78dfbDZbhBhB5ush4rn6wXa3Q4gRzlks5jMsl4tSYg+A2Gvo2FBRvgelCZkx6HdbAIBzz9N0t+aNlL6rGo0ql6RN9GKO6dwixBgQQgAIsMaUNLBSKv2X9ldRFEVRlPN5nlGy8g3aZV8pi7dZb7DdbBFjkFDx9uWnCpvkp9Jql/cDnLOYL+aYzWQ1jJkn7398tVFRbousOG+3GxhrkkfAMyGzyrZqhaJcmtbrKaRysADgrIUxNuv9iqIoiqJciN/oV9XJ7QWp4VoEsJiBrtcbDN7LY0msYC7DwdPbi5yqVBJCKrMXQxQT0TKgTP+jCfu90vdTlOegvUoIMUjq12zWlUiHZyKX6syiq6JcEpZODWTEU8oPA2JkGGthrVQkasOJnuzyUhRFUZSXQQWOp+SYfMDpGSpRFQyg73sMgx8ZfebRXdnKCfnNGCrPEwh+CNhud8XHY7QHe9EciqJ8BhGhH3rEGLGYL5rHf3GnLgRNwsWMEaPRZxFw6MR/ym0QH43UvzEQvEeIEdYaWNtUTCmmos9x7imKorwGdOJ/5RVRgePFYAm1qPdixG4nEyeiyenwHZM/ZsSUN8/M6PtecpvTFmosB7S9UZRDHKqIzCguS7vdDsYYzGazpD/SE64yi9GolIr97X1RnoX2VPLBYxg8nLVS2at9PuvvpGW8FEVRFOVy3FaAUg+Op+aAeUZb+g5ATCklpRpDrtmaXiub+SRFJUd+5KgQZvgQwCECXbF2K9ticDFy0zGkojS0QU8EETdIrqndbofZbAZjDThyU33ktrt3Ll+91qlZRSf58kk4/cZGlJekdF+03/vl8uTBJ1NR50BkUmWvMaWY+skqRYqiKIryu9xLjZi2vzw8feRSMW86dm1N5ffGtm1n/o2ddjpifEZOne40elWMjBi5VFUh0PdHdFRFjlz9gGNE4EN59Fwr0yqKcpp0scQQsNvt8D//5//T6I+/EeJw7Qu3Go3GlEIQ0QiozV6c/vaXPzaaUnLftNr8ZEwFoirmA8Cs62CSoj8qADv5iVXcUBRFeWy0vOwxLj9OGlfFq48xM2KMYB77M6ZXwhgDIkpVN81hn7my8jC5fwSN4Hg5cvREuuSJwUmIOHsAz1lxy2Vn82BTY8wV5VscuQS3ux2c6+Csra97ystLGhAiU4xGCRi3JfyD2ujKU5N9NqTUMEoEbGRGCOIz5TpXRDRgHDSlKIqiKMr3adehckRGjBEheIQQk8Bx2F8t993GWFhrYIxNokezTdC3RCcVOF4GGv2R263RXVYpfrJ9Hn8MnZqF6bDy5XjaSfklIICmB6fK35Ke0sE6W0Lqn+1QtiGNxhC8D0WMlSfKK6Fth3KMVCwFSBGJzIzgQ4oIcrCpikqbrqkoiqIoys+ofSojhIBh8AghjHzV2n43ix25zw4hwnvAWoeu68QEPA14mXls2/HJUFAFjqeHJvdaBUzy+K01GHxoCrj+4NOIwBxBxsCQgc5qlRF6OnyB8XUYQsQwDHh7eyvpX880v8+GqULy7CEDwCNyhKU8IcWXOzbldZEBU067BEIIiDHAGAvnUgRUYzOlGoeiKIqi/AzpT6Vz9T5gGHp4X4tNEEl0bk5HKdaNTfqKRHgQYgwYBkaMDq5zTYVPFEuFz1CB48UQg08u5fLIGHRdh12fjEZ/sN2WznUwVov0vDw6eRjzneORYuyHYQAAzOfz0VN0MkLqsZhOMnMHGEOANWbyNWtHd+nzS302HpdxcGIKjw0BIQQQURU3IBFCMrDK73yO60hRFEX5Duf5cyj7ZMHCew/vhyJuEJlSkj3flj5a+l5mSWWJMaRUlpDuZ78OoOs6kKEyh/1KARYVOF6UPKSzqeykWW8RzvbMGHvV51KWxS9AeU10rng+TcmQfhhgjIFzDjFGmMZ86RlXoJllAkpECCGi68bP/0b1GOXRIIQY4L0HkYFztkR0EKYO73oyKYqiKMpPYJaIySpuyGKVtQ7OuSZyoy12QTAkr2O2sDYWgSTGiMgRgx8AkoXzEr3xhQWuGwocjzKIeLLZQnsWjE6IXEdPTjYyBEROkRg0HQHW97e/Y1n4oqzDgZkx6xzm85kaub06RywlHoNrtFfHD0CZdOVrKkVYRWZ4P0jnkCpBwMh2fjLJ/43W+LufaYyREtZAOUAlwkIX3V+SQ+bp075FTpWIkM4d52waPLWu7rEpR/x8IqGiKIqinOISQ6iSNZxSTcRQNICZYa0szFnnYE0u2kqIJSVFxnTZYJSIYK2FMVJNZRj6Yk4avIchA9e5L+/8bSM4bj6IeNFRSxqxFdGhKf8qTxkwCN577HY77PoeXdeBeZBwXiC9lkerXUIqJ9scWqLk7cEBxhJWqyXm8w6ocskBXvS3eWXKefQgv/2Zu3n8bada5OQ9gVqumcFJyfb4MAZ0bYORs3S95nmGUkd0ncSKPArtWQqB42GA0RrzovI13T3+nW8SIYLie7+5CA6yEXg8HtF1bbpFfLwKfn1MDAzGUJnNeL0YPLqW0sMqKm4J5RNFh0mZ/tjnvig80zPZec/PhIqK50BtvWkkVfEK8W5+npYqwttSc1SCo6Ki4tXIwboPpTktPDwEF4Vp3DqSj587a4FGYtUxwhfrsA+eRH/NhvMxxVlnrTQNMrSCEALH4xFt24bzW4vPaRNsEwKVg0a/+ayiBGMM0zzjdD4TuQGeBojlBD6Np7aDsj0SmofglK+MMxel8PvpCX3fXRAmcYu3NTf31rbymvWCNsNjVZ2mLG16Ufa0+DxSBD7fBuFvJBw4qS2CQ3uZ+rGqLsJ5uK8yaZG2w7ZntNJabE59O0gvvirIiJeTEkV/lXZZtL03NBgfCJ+ocOOMoW3JwNMbC8/Ih4YMD2XiIjyiKs7Bg6HvWxhzIM8ObfcPHUAu6XrtYIvzC9PVRCABw9Dj+HCAEHnAt+YDfVBz0FulFNSicgAczo2Mmm/r7qr4mViRkoHo1FqDsyjNBzinOICzjW9YRcW3BHXoQhBdoLUBYzbF8yWpcUvkBsAqwbGHa+Zn21KQIfYLM7R8FSbc1O9c8Xr8sHhrG9RvByXlMvEz8towcM6CcY5GyFRVJLK+5fJx0GKdXY8NyolyFIZF6d/3HvyXzPPzy1BAQ4NqtShM8wRtDDjjOBwONAMp5bMde+4X/jwn/FORmoJHQVRwWOtweiJD0a3haG4S22vJrnxf/hLrgZ13niqznM94eDhufioyuOSMIz8dVszKleb5kqfBNWXITvsoxud/qjrBNsdUXrZIDCWyIhAS1tqV0mJFXFiHa09GhiLNg0WyAsmzAgVhceFhQSezo9p49vSewTP31TOMg7900vzTKgFsJXh4lip44S1fqt68JzVc13YwZgICcTSOI7quRSsFtWRW9psUcR4OA5xz+P10gjEW1CeH1K7ckMLBpdNZnVv0P6HBnAdA5qcPD0ccj0fKz/abeyG0Gc55UPZQas00zTCWAmNrLNquRdu2qB5EFZ+L/Xhi+3xhAIx1UEpTBTQhabLCA1m9UVHx3ZEn4ahgAJmORp+qqNiLKstbQiU4XoF1DBDNwFYfXuC2fu6Kiktce1CnFJSteZynFA1jyA1fCFJtUKWFHJzTskGgzIqyUzbk0IOFsWwOsqOSI9r1JmPOD1A4JEeAMGgoB4XxHDhn0ErjfD5jUQqc81TytWmajVLlemdQ+4EXguUX3nuM44RFqVWbWuNa4937vlAHrZYJ33qPp9MTurZNZnIs/uOyImG1TZZa6s5Gy3sg30/r42SJbIh1QiJhkLcYlAqMrdb2Fy/yeVjv4KylyiHWJrLCWKoWQuabLqdilIF9SKdKKgrQ7GUuccqT+oIxDi7W1UJyOlYeSK9+uz/yix81cti2n/fZz19tZedalKlwAFKfM05zug+WRWGZF7SPRyIUYp3kQuEihMDj4wPAGMZxwjzNcN6AMZFmny9K7iaugrYSKz9R+7FoQire8eGAru3SKmuTUBbUJKH/ZhxKKUzTDAYWKsIwDH0XDOwqKm4BeabFOfJ5MsbAWIPDMOQ+bMOxV56j4qeAsVhZxUFrEwjqPA6+Hqd9DSrB8Wa8ZBa4ouJ+EGPUtVQ7P/Rj4G2Mhgkz6lLKlJqxHSiFrYZtUccnOM3ewRfaDLYeCMZ8/HAUaZD13ndarNrCOAOLpa58nI0GtDGYpgnzssA7j7ZtMQxDLokYtnFrnfq9YUtdxQGadQ6n0zmYEvK8NHtB37tSgjy3t7gZ+myZFWaliODwHskvMZET221Gi83rv/+zbcMTeVGWySwVTB5IaR+l2sJZui+dc7DB+yamjthQ8jSfbSZ2oj8MYyyRkpG0iKVgcyWRPbVF/H1KSufa+UUyKfYh6ZJ9IF5DYrzDPXttEy89x1VzvCTHAGobQ9/h1EgsSoExqkYyTjP6giRgQQJVth4hOB4fHtBICSE45pnMkK3P5FW8n8pAlaFMOaL0l67tcDwOSbnBGYIvSFCbbI4/khtUOnuG0hqcCxitMQwdhsMQyjPXGKriM/GcWqv0zvFQywJ4QDbNZuk/qO0qKr4pYuygtYYxJvjtvVyh+HmoJqPvhjq8qbhX/GlsHmXNMR2FyhFySElGoojSTp+j9ZiXV6pAqGPk0FqHoFnQgC0E5jG42JfCv7+CI84yexf3zUPOrcGyLERseIdGNjgcBmKrEcqRpjJZ/mKAdz3FrWIf6982qmmMMVBKkUpileR55UlaEBWpSeWPruxvvT3nPZZFhRk7gIV2yTmDczRQ3ycsdtpAuf+k0vDpNYBARiARGK4sexq/Q+lrQSeW7y+2+p8LjrbriMTg2Yiz9LaIhMq2Wkg0hNy9sH7zHutf7BpixYGva/0v2e9fRGZv4U9esrvwA+c0FYnjYYC1NpHL8zzjdJL49euBytxuNk33gIcQ5JfRNBLzPGOcZmilAymGRIps5fmMkYJDyAZ93+HQ92i7JpmCxvSVLE0u7iNHKg3ngWmaMY4T6DngIATHMPRo26bYZ50Lr/h6pMcMZ7DWYVEKTdOgkdEzbLX0ikysqPgp4JwqqxijwTmDlA28d1dio69DJTheixgbX6rVKiruFlv2tRyU0CyxSeX9omojPtizBH9tOJQUICwMdDiDkALLokI1DBSDgHLmJObkf+AJh0Mr92uMwaIWLAuVMmykRN8fU2WUbJwYz7c8yJf2BLf1ALg1xFDRI7jXe5/UDQV/dgmf199u61luzK+WhPcsueY3jQyf7eWYFqSDz4RGrA6S5Pm+LHXqkhojbsOlVIQYKgfSISglKLVLpDKnPJASInhbXJAccX1gRV5cXKidz56fSd9r539uy7cW8Hwqnrmc14dE0ZhzPUPMgqfGvCxkesglrPU4jxParsWh7y+UICl9zgOcebSNhJRHdF0HrTSU0tDGwDsHG5VoiGlINEvXtg3atkXbSAgpkrIjKYJ21EtJ9MQY1LLgdD5DG02Eh3Wk3hh6CEHEN4+k+Guvb0XFByD29WpRMMbg8fFhk4q6h73vK/FR8T0RVRzeOxhjALAwJviAici/QCU43oj0E/pr87UVFfeBclyzJidcIDcsnCPVhhAyOfyXo0p2ZXthmBaKCzBwLuDgoZRe+XOUJR/LDW029SGw1qYccesspGzwcOzRtl0yTIyzldtjiIqO59MYKl6C/Gik9qW1Th+u2ltYZvt+rShgKH6dDS4HZOk1o3KWSukkA/EuEBEuz3T7IiUklklOe4/pIeFvbNO5tGlM/cA6DSQQGHspIdnb4g+3QbosbCNj2YQeORchXbvr4bnf+fZW8RZJxZ/wN1KMP2FPweBTP4yCDGsaicNhgFIa1lJ7U0rjdDpDcJ5T58rD8kBZPUZwDtG1aJsGfW9z+lMg4YBY1YbaqQipSrSNQPDGfayEPcV5BIVQPLZlUWnQKKWgctpdG5a+JEgqKr4K8TnjPbAsCxhjyWumoqKCEBXZUjZQiohAzkPMdUPdeSU4Kip+NFgxgKf3PhgQWuuCW7JA08hisF+sXSqZEmGxket7CsStsZiXBUppPLkTYrpBJDakFEkOmg0809bfHd57LMuCcRxhHSk2Ho4PaJqG2OiUWhDPdTNcjjP3ec6y0htvQKImykvpPYw14fpmgsCXA/PdyhdXhul7Ewt7PxSj1JjTeYScYylZ+t1ZOI6SdOBByx/rwtNnPKWHMLDsbcFZUmUUu0vn9mc817p88YcBcMVpR9pofZ4Xa5dj7d393lDkchVXTwI3eWey4u+qK6F0DuITePjK43g4QCuD37+fUpuZxgmMAf+wX+j7NikxCm6EdlEQtURgyNXvXrp3lIIdD7euJlu+vjgRUqA453E6jTidR3iX07qGwwGHwxGCc3jnkjLuHlpWxU8BtV+lNYQUkI0sUu0qKn42SpU2QFW+lFLQWqO9MTLwjQTHR8yS3AtyoOfjL+2vB6jPhVsV3x1vDajfv7XE4DbNbbNShZFniwHAeQejDZx3kFJmw0GsB5jMo/ismMsrNrkojWmaMYcSgdaGag5mWR1cPCbBGWQj0XckY+7CTF/0I4gz39fV9FkVkk+PpdlHEQLqeSZiw1iDRjYYBvLYWBumXm75+m/zZ5m13yxx3ZzxOWx7FF8QMK/dzIUc5S+O52Ub2Ls+qSRm+u2ip0vU/5RE0oUw4Y97TmOxnZ1frOcj6cbRtk36fs90kzEOxovfkZVHWL5/L7zmubtDavwJm4X9c1/eLD7iON+fGElb3JJ0kWwo0kByYWIPIRh+PR5hrMH5PIZlGMZxAWNnMDDytkBpfhzuoOImuFREba5c0T9cqH7gAV8Q2YWxKWMMxnr897//4ul0CsEwEYVd3+J//fOIRlJpQbY6t4qKr4f3HjxU/dFK4X/+53+Qq6ttW+mfWm1t1RXfD2VqdoyFmqaBMQZaKzRNu7NWmeb7nrh+j3lUBccrkR/GO/NfFRU3Co9URbD0MwjhpXcOjNPn2pAzcvLa4CKvk6YCkf6m0DYaFHqaqTbWYTyPOI8jlDLBoDEeDbDakM/HY62DNhZKaczLgsPQYzgMaKSkmcByf2Gd9TmF83U+SKtZ8nGI5RXHcYTSCkJIHA9HNE2TiByg8Ej4U2d8I2O+Nz0znltp35AlfFXKd9ZDIL/6Za/1kJelTjlnQXMQ/vpQCQTr/eZ2+JymYO+8XkIlxQc2kRl93+Ew9KTQSDRLuWhBNBUpABUVGdda3dsaS6QqmqbBw/EIZx2meUk+MOfzCO89/vn1gL5rqX92pfFbbrN76jjvt59t+vsLLjN4zTgPFlRKWhv899+nUNKW1nFwaNsGvx4f0JRlYeuUeMVf4X3vr0SuA5jnKZDce4O1ioqKEjGlkUzSbSglu47Nv8KPqxIcr0Y5U1tOJdeHdcXtgsZprJj9puEhZzSJ6KyjlADnQ7oImcoluTPWg7g0q17O9AUyQS0Kp/NIRILSNNPN+Mp09LkjJYWHDYywgbEOx8MBXUul2pJbM0vMSDqMslZ9WW5TaY15mjBNE8AYhqHH0A9FagHSQKG4YneBkthZY50OseZtngkC2cULpBnglXFloSIBkFI4sCFCkNVD0bvCOfKx8KDqPGVJyliBYZ7nvKdIoAVmYzMEez94B4C8B6haRJT7l3RKnlGPB/M2NU5FxctR+rIcDgMAuq/mRYExqvpwejrBWoNfjw8Yhp5I29VNQm12Xd56j8EI71mhgEKxaCDNAYBx6tvnZcbpfMbpNCKWP/ZwaJoGjw9HHA79yvi0ouK2QH28MQbjOGIYBkpV9X+KWSoqfiqi6pZDCHpPVb74KqX4q8zGK8HxWlzEAptZjoqKm8O+lNiDAmTrLKyxQQItIKQgY8RnBm6ZIynVEwxKa/z+/YRxnEg5wQUQOrrryqcyivZhEE056FprPD2dYIzB//r1C00raXvIxEs5o+/CzH/sUI0xmJcZy7LAO49hGND3PZqmKQbLnsa16V7+HC+N8hxePkDemY2NvIJfExLbq539HkoC4uX7ZQyp6kdZNSQSQ7G8aTYtDCacoY1tzTfjwURlUfzNpCA1Tdf1MONUtAeP6NvykSDDz5gm4wDGg8opuoTHK4Kd50FFRcR7N4oYMBIB2PcdnH+E//0EpRQEZwDjmCeqAmWMxTAMaKTIJG46LA/GOCLhsbcvIHDIG4ZjayJtrMU4zTifRzJmBIcLHjCR3DgeD5ncqKh4F7xvW4o+G0otcM5hGIagLKzhfUXFn0AG6gLGGBhjUhW6+J1zn19GthIcf4NCwFE7wIrbRum/Qe3WOgtnLWyQlEkhidiIaoZy4F1O9MUtsizd55xc85+eThjHmcaFYXaQBrkoguzN4HoTMCMG1aAA3FqPaZohOMfjI5mApq2ktAUW1CZ0/MZYLMsCpRScs5BCoj/2aLuOVCtAvnl9QbBE6fUn3dFlfv1Vwe31jJHkVbFema77lu5Yv8nEVElSxNldwMM5UsvEEqfbEqixgki5btx/zNNMpIWUdFTBx2LlZxE+jyVQU7lhznEepyCz5xfH/lFomoZSswAgzESAx8HgBnW8VvFJiGWHowyPc4bjoQe8x9PTCUrrRAIqZfDv7xMWZfBwJEJXCk6pJIm1QO5MNvdv2ifY5uPM6FlL1YamacE4TdDahO8c4D3arg3KjQFSimDYu95+RcWtIFYgmucFTdOk9JSaSVVRsY8yTTzGdUIIWGthrYUQMiz3NSa9leB4C0pSo6otK+4CYdAZZtetc3CWZtWT1wZn5eI0RH5JJgOIUDidzjifznAOyR0/KkY4z4PpF2XORlVo6BWd9TifR3DO8fhwhJAypKrkI6ESnxbLPGNRVJpQSoHDMJBhZJBr52wDSnPJGRlllZRLBcRXIT8YLo+Hxv0sKVGQSAokhU5UUkSFBeBhU4oIksrChaoNW5+NkixhPCsueBgw8fRwo8ozayPOQIKxKGUMW1s97fL+Sofutm0gpYDWFgzuyhV4X3h4dC0NBvMxXZvDu432UfGDsDJZJp+h44Gk9KfzGefzGdEAVGsDa8/QakHbSvRdj7ZrIaVIptHrGZp1fx/J4kR8OsB5MorWSgcD6QlKm2wU7AHOgG7ocTwecDwcIDjLaYW0qbTLGjtV3AoYGLTRUGrBYThAiJhWW1tqRUVGjo1zGkqM7xiEECnNnDGeK2VVD47bx/Wurga7FbeKrFRwjspvOucgOM/mmkW6xioQfaZZx8Go91Sq8Hw+BxkaT7N1jPm0rUtfu/JO2phIxtU80oDaWoPz+YSua3BoZBrEM0YeIvM8Y55nWOvQNi26vkfbZPPQtRQiS7b97h39uffzS1JUstLCh1Qc8q1wQUnhnCOPC+dXy3pcKiwArAgIxlkylE1lTsPnMZ+SMSQyg9YPR14QMPvPMLZzja8FjPkYpZTo2hbWTADIKHbrBfPe4JyqUHDBKT0lKIM430rzKyq+BtG8jYGFUqsMQ98FFZ7AaRxhtEkpIUopGK2wLAtk06ANs9NSSiI7UlnjQtCRUvcCIW4tjLFQWmNRC5EnxsIltpj6GiEEDocDHh6O6NqGVFmIZHmh8Eqv6tCx4quwfh56UGlY5xz6IXvclJWIKioqgOcGB5HkcM6F0rFtiJ8+f9KQuf/7f+pd+5f4KnaqouItyMY/wN91OLnDIvfkUoIcUy/YxbLPBwpsd9nSFYNKuV4GHHFgDwYK2r/tPelXg+14pROPtXm/wkaRcf0zlr96N1z73a/vxQbC5tPAABHTZRK2D+bbUfdUVJTwoQLRnikugNQnJqKSvYRaxZoo9bnSVOyd06tCqvzyO+QyNaai4isQJw/IYLq2x4qKt+KrDUYBQH7bMcAn4vsOpCq+I96vvebtCMFp1vszsXK/Y+Cc7cYk3+/uzEqJQiz4iXv/+DXjrypEdOf+fLCdV1myXHOzKz4XW5pvXyzFIPnf3zAX2y78lv5mW3sqqHofVdwKiKAT4fUXH0xFxR3jFsbF/x8itjnVhUey7wAABG5pVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+Cjx4OnhtcG1ldGEgeG1sbnM6eD0nYWRvYmU6bnM6bWV0YS8nPgo8cmRmOlJERiB4bWxuczpyZGY9J2h0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMnPgoKIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PScnCiAgeG1sbnM6QXR0cmliPSdodHRwOi8vbnMuYXR0cmlidXRpb24uY29tL2Fkcy8xLjAvJz4KICA8QXR0cmliOkFkcz4KICAgPHJkZjpTZXE+CiAgICA8cmRmOmxpIHJkZjpwYXJzZVR5cGU9J1Jlc291cmNlJz4KICAgICA8QXR0cmliOkNyZWF0ZWQ+MjAyMS0wNS0wMzwvQXR0cmliOkNyZWF0ZWQ+CiAgICAgPEF0dHJpYjpFeHRJZD40NjkwNzJiYi00YmUwLTQzYWMtYTVlOS0zZTIyYmQwZGU2MjU8L0F0dHJpYjpFeHRJZD4KICAgICA8QXR0cmliOkZiSWQ+NTI1MjY1OTE0MTc5NTgwPC9BdHRyaWI6RmJJZD4KICAgICA8QXR0cmliOlRvdWNoVHlwZT4yPC9BdHRyaWI6VG91Y2hUeXBlPgogICAgPC9yZGY6bGk+CiAgIDwvcmRmOlNlcT4KICA8L0F0dHJpYjpBZHM+CiA8L3JkZjpEZXNjcmlwdGlvbj4KCiA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0nJwogIHhtbG5zOmRjPSdodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyc+CiAgPGRjOnRpdGxlPgogICA8cmRmOkFsdD4KICAgIDxyZGY6bGkgeG1sOmxhbmc9J3gtZGVmYXVsdCc+QW5hbGlzaSB4YXJ4ZXMgc29jaWFsczwvcmRmOmxpPgogICA8L3JkZjpBbHQ+CiAgPC9kYzp0aXRsZT4KIDwvcmRmOkRlc2NyaXB0aW9uPgoKIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PScnCiAgeG1sbnM6cGRmPSdodHRwOi8vbnMuYWRvYmUuY29tL3BkZi8xLjMvJz4KICA8cGRmOkF1dGhvcj5Bbm5hPC9wZGY6QXV0aG9yPgogPC9yZGY6RGVzY3JpcHRpb24+CgogPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9JycKICB4bWxuczp4bXA9J2h0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8nPgogIDx4bXA6Q3JlYXRvclRvb2w+Q2FudmE8L3htcDpDcmVhdG9yVG9vbD4KIDwvcmRmOkRlc2NyaXB0aW9uPgo8L3JkZjpSREY+CjwveDp4bXBtZXRhPgo8P3hwYWNrZXQgZW5kPSdyJz8+2CJOQgAAAABJRU5ErkJggg=="></p><h5><strong>Una introducción limpia y rápida</strong></h5><p>El análisis de redes sociales es una <strong>herramienta teórica y metodológica</strong> para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.</p><p>En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino <strong>cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales</strong>. Acompañaremos <strong>el recorrido teórico y metodológico con sesiones prácticas</strong> para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras. </p><p>Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img 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"><img 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"></p><h5><strong>Horarios e inscripciones</strong></h5><ul>-<li>Sessiones presenciales.</li>-<li>Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.</li>-<li>Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!</li>-</ul><p><br></p><h6><strong>Primera sesión: Introducción al pensamiento de red. </strong></h6><ul>-<li>Martes 11 de mayo, 18h</li>-<li>Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento. </li>-</ul><p><br></p><h6><strong>Segunda sesión: <em>Hands-on</em>! Instalación de herramientas</strong></h6><ul>-<li>Martes 18 de mayo, 18h</li>-<li>Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.</li>-</ul><p><br></p><h6><strong>Tercera sesión: Análisis y modelización de redes.</strong></h6><ul>-<li>Martes 25 de mayo, 18h</li>-<li>A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades. </li>-</ul><p><br></p><h6><strong>Cuarta sesión: Social Big Data y análisis de redes sociales. </strong></h6><ul>-<li>Lunes 31 de mayo, 18h</li>-<li>Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .</li>-</ul>
- +<p><strong>>> ¡</strong><a href="https://canodrom.decidim.barcelona/assemblies/comunitat/f/2030/" target="_blank"><strong>Plazas agotadas! Apúntate a la lista de espera</strong></a><strong> <<</strong></p><p><img src="data:image/png;base64,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"></p><h5><strong>Una introducción limpia y rápida</strong></h5><p>El análisis de redes sociales es una <strong>herramienta teórica y metodológica</strong> para comprender las estructuras sociales, como emergen a partir de los comportamientos individuales y a la vez cómo los afectan. Gracias a la profunda penetración de las plataformas sociales en línea, hoy en día podemos observar y, a menudo, analizar enlaces a una escala que supera con creces lo que era posible hace sólo unas décadas. Si bien esto impulsa nuevas metodologías, las redes a gran escala que podemos observar aún pueden ser informadas por preguntas clásicas en la teoría de redes sociales a la vez que también estimulan la producción de nueva teoría.</p><p>En este curso realizamos un recorrido rápido por las ideas y métodos clásicos de este paradigma. Demostramos como no solo son aplicables a las redes digitales contemporáneas, sino <strong>cómo se han desarrollado gracias a los datos que podemos extraer de Twitter, Facebook y otras plataformas digitales</strong>. Acompañaremos <strong>el recorrido teórico y metodológico con sesiones prácticas</strong> para aprender a manejar herramientas de análisis y visualización de redes como Gephi y otras. </p><p>Curso a cargo de Emanuele Cozzo de tecnopolítica (IN3/UOC).</p><p><img 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"><img 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"></p><h5><strong>Horarios e inscripciones</strong></h5><ul><li>Sessiones presenciales.</li><li>Martes 11, 18 i 25 y 31 de mayo. De 18h a 20h.</li><li>Inscibete en esta misma página. Quedarás inscrito a las cuatro sesiónes de formación!</li></ul><p><br></p><h6><a href="https://diode.zone/videos/watch/4ca4d8be-479e-4c6f-9b53-bdcd0ee10078" target="_blank"><strong>Primera sesión: Introducción al pensamiento de red. </strong></a></h6><ul><li>Martes 11 de mayo, 18h</li><li>Introduciremos los elementos teóricos y metodológicos básicos y veremos cómo estos se relacionan con las distintas escuelas de pensamiento. </li></ul><p><br></p><h6><strong>Segunda sesión: <em>Hands-on</em>! Instalación de herramientas</strong></h6><ul><li>Martes 18 de mayo, 18h</li><li>Sesión práctica para conocer e instalar las distintas herramientas que nos permiten realizar el análisis y visualización de redes.</li></ul><p><br></p><h6><a href="https://diode.zone/videos/watch/86ce06ea-b24b-4841-8409-52ef70655b35" target="_blank"><strong>Tercera sesión: Análisis y modelización de redes.</strong></a></h6><ul><li>Martes 25 de mayo, 18h</li><li>A través de algunos casos de estudios profundizaremos en algunos de los elementos aprendido en la sesión anterior, como por ejemplo las medidas de centralidad y la detección de comunidades. </li></ul><p><br></p><h6><a href="https://diode.zone/videos/watch/2deae660-72d8-421a-8da2-efdbc1904648" target="_blank"><strong>Cuarta sesión: Social Big Data y análisis de redes sociales. </strong></a></h6><ul><li>Lunes 31 de mayo, 18h</li><li>Veremos como extraer datos de las plataformas sociales en línea y cómo analizar las redes de interacción social que en estas se desarrollan .</li></ul>