crowdsourcing activism

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  • Crowdsourcing Activism

  • Activismo y Redes Sociales Redes sociales ya son usadas por los activistas para compartir su visin y reclutar cuidados para su causa. Pero activistas tienen que invertir mucho tiempo en estas tareas.

    2

  • 3

    Bots y Redes Sociales Gobiernos y grandes organizaciones con mucha experiencia han estado usando los bots para callar discusiones, persuadir y cambiar enfoque.

  • Problemas! Usar redes sociales estratgicamente es complicado: requieres

    personal con mucha experiencia.

    ! Reclutar gente para una causa social es dificil: toma tiempo es tedioso poco productivo.

    4

  • Botivist: Usando Bots Para Reclutar Ciudadanos que Participen en Activismo

  • Botivist

    6

    Ciudadanos son reclutados para contribuir a una causa social.

    Causa Social

    Estrategia A Estrategia BEstrategia DEstrategia C

    Botivist

  • Estrategias de los Bots

    7

    Estrategia A: Directo

    Estrategia B: Solidaridad

    Estrategia C: Ganancia

    Colaboramos para combatir la corrupcin? Cmo reducimos la corrupcin en nuestras calles?

    Colaboramos para combatir la corrupcin? Cmo reducimos la corrupcin en nuestras calles? Hazlo por ti, por mi, por Mxico!

    Colaboramos para combatir la corrupcin? Cmo reducimos la corrupcin en nuestras calles? Juntos podemos mejorar Mxico!

    Estrategia D: PerdidaColaboramos para combatir la corrupcin? Cmo reducimos la corrupcin en nuestras calles? Sin colaboracin, el futuro de Mxico es negro!

    Usuario : CherryRexContrasea: Pi3.14080878

  • Botivista: Funcionalidad

    8

    1. Identifica posibles voluntarios de Tweets que usan ciertas palabras claves

    2. Bots mandan tweets a ciudadanos pidiendo accin inicial usando cada estrategia.

    Estrategia A Estrategia B Estrategia C

    3. Bots ensamblan activismo con ciudadanos reclutados, guindolos y pidiendo micro-tareas para la causa.

    #Ayotzinapa6Meses este gobierno corrupto es el responsable ya renuncien!

    Descubren mentiras del corrupto de Osorio Chong!

    Estrategia A Estrategia B Estrategia C

  • Interaccin Bots-Humanos

    9

    ! Bots evolucionan su relacin con humanos mientras crece relacin con el bot.

    ! Maquina de Estados

  • Botivist: User Study

  • Metodologa: Estudiando Participacin Ciudadana

    11

    ! Cada bot interaactu con grupos diferentes de ciudadanos. ! Estudiamos tipo de participacin ciudadana creada por cada estrategia.! Analizamos: numero de respuestas o replies numero de retweets, number de favorites numero de personas participando!

    Corrimos una prueba anova, asi como comparaciones directas por pares para ver si haba diferencia significativa entre estrategias.

  • Resultados

    12

    *Bots que son directos generaron:! Mayor nmero de respuestas de los

    ciudadanos.! Mayor numero de voluntarios nicos.! Mayor numero de interacciones entre

    ciudadanos.

    Strategy

    *Bots que muestran perdidas generaron:Mayor numero de interacciones entre ciudadanos

    *Bots con Solidaridad generaron:! Mayor numero de retweets y favoritos

    al contenido del bot por parte de ciudadanos.

    Numero de Respuestas Ciudadanas por Tweet del Bot

    Numero de Voluntarios

    Numero de Interacciones Ciudadanas por Tweet del Bot

    Numero de Interacciones entre Ciudadanos

  • Metodologa: Estudiando Calidad de Contribuciones

    13

    ! Estudiamos calidad de participacin ciudadana creada por cada estrategia.! Analizamos: La contribucin del ciudadano es relevante a lo que pido el bot?!

    Reclutamos 3 crowd workers para categorizar cada respuesta de los ciudadanos en si era relevante o no: contribuye una idea para compartir la corrupcin?

  • Results

    14

    ! Overview per strategy of the percentage of on-topic audience members.

    Majority of contributions made by recruited audiences were relevant for

    their task.

    * Almost 100% of the audience members who engaged in Direct

    Strategy made relevant contributions.

    * Audience members engaging in Loss Strategy made the most irrelevant

    contributions.

  • Methodology: Most Active Audience Members

    15

    ! We discover common traits of the most active audience members recruited by Botivist.

    Identify highly active audience members.

    Characterize highly Use Mean Shift to cluster active

    audience

  • Results: Traits Most Active Audience Members.

    16

    The Negative Nationalists Most tweets used nationalistic terms and had negative sentiment. Most audience members recruited by Gain modality. Negative people recruited best with automatic agents who share

    hope messages.

    The Community Nationalists Most tweets were about social issues and nationalism. Most active of all. Majority were recruited with the Loss or Solidarity mode. Showing solidarity and what could be lost without participation was

    effective to recruit nationalistic concerned citizens.

    The Short Lived Activist Less than 3% of their tweets referenced political content or

    social issues. Majority recruited by the direct modality. For Individuals with no political affiliation, direct messages most

    effective to foster their participation.

  • Qu diferencia tienen ciudadanos que contribuyen con bots vs ciudadanos que no?

    17

  • Identificando Diferencias entre Ciudadanos

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    1. Tomamos todos los tweets personales de personas que bots tratamos de reclutar.

    2. Usamos un Mann-Whitneyrank test. Para encontrar palabras (hashtags, usuarios, palabras) que son usados por un grupo mas que otro.

    3. Categorizamos palabras encontradas usando Topic Modeling + Crowdsourcing

    4. Medimos que tanto cada grupo habla de cada categora encontrada.

  • 19

    Categorias de Palabras Frequentes usadas por Ciudadanos

  • 20

    Diferencias entre Ciudadanos que Participan con Bots y los que no.

    Ciudadanos que responden a bots tienen a usar palabras sobre activismo, noticias y marketing!

    Ciudadanos que NO responden a bots tienen a usar palabras sobre poltica y noticias.

  • Botivist Research Takeaways

    21

    ! Automated agents can be used to recruit citizens, and incite collective efforts for an activists cause.

    ! Citizens react to strategies differently when coming from humans or automated agents.

    ! Specialized citizens engage more with specific strategies, e.g., solidarity. Citizens with general skills participate more with more general direct strategies.

  • Backup Slides

  • Talk Outline! Problem ! Contribution! Platforms for Engaging Online Audiences

    ! Visualizing & Engaging Online Audiences! Engaging Online Audiences with Automated Agents! Engaging Online Audiences Opportunistically

    ! Online Audiences Future Work23

  • Problem

  • Traditional Media Content Distribution

    25

    ! Information is filtered through hierarchal organizations before reaching the audience. The organizations focus primarily on commerce.

    Spreadable Media, H. Jenkins, et al. "We the Media, D. Gilmor

    Gate keepers (e.g., Advertisers)

    Organization (e.g., TV Channel)

    Content (e.g., TV Shows)

    Audience

  • Social Media Content Distribution

    26

    ! Participants are peers and can change roles. ! Content is unfiltered before reaching the audience.

    Community

    Audience

    Reporter Publisher

    Reporter

    Advertiser

    Editor

    CommunitySpreadable Media, H. Jenkins, et al. "We the Media, Dan Gilmor

  • Problem

    27

    We lack understanding of the new relationships, tensions, experiences emerging between audience & content producers in social media.

    *Pasquali F. et al., Emerging Topics in the Research on Digital Audiences and Participation,

  • Some Consequences

    28

    Controversial designs.Non-useful tools.Limited Interactions.

  • Example: Problematic Design

    29

    Facebook gets sued!

    Facebook designed in 2013 organic interactions with companies via side stories.

    User A Company

    User B receives sponsored story

  • .

    30

    ! I use social media to understand the experiences, relationships, tensions, and interactions emerging from content producers and their online audience.

    Friendly-Intimate Spaces

    Adverse Spaces

    Controversial Spaces

    ! I use the understanding to design novel tools to better engage with online audience.

    *A Rhetoric Of Motives, Burke,

  • Main Research Findings

    31

    ! Online audiences and content producers interact in a gift economy focused on reciprocity and collaboration. Multi-faceted data visualizations and online autonomous agents are tools that can facilitate reciprocity and collaborations between audiences and content producers.

  • Impact

    ! Opens design space of systems for engaging online audiences which focus on cooperation and reciprocity over profit.

    ! Interactive systems focused on using the intelligence of the audience.

    32Jenkins, H. "Interactive audiences? The collective intelligence of media fans.Baym, N. et al., "Amateur experts International fan labour in Swedish independent music."

  • Talk Outline! Problem! Contribution! Platforms for Engaging Online Audiences

    ! Visualizing & Engaging Online Audiences! Engaging Online Audiences with Automated Agents! Engaging Online Audiences Opportunistically

    ! Online Audiences Future Work33

  • Engaging Online Audiences

    34

    ! I proposes t