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Crowdsourcing Activism

Platica sobre como usar bots para reclutar ciudadanos para el activismo

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Crowdsourcing Activism

  1. 1. Crowdsourcing Activism
  2. 2. Activismo y Redes Sociales
 Redes sociales ya son usadas por los activistas para compartir su visión y reclutar cuidados para su causa. Pero activistas tienen que invertir mucho tiempo en estas tareas. 2
  3. 3. 3 Bots y Redes Sociales
 Gobiernos y grandes organizaciones con mucha experiencia han estado usando los bots para callar discusiones, persuadir y cambiar enfoque.
  4. 4. Problemas ! Usar redes sociales estratégicamente es complicado: requieres personal con mucha experiencia.
 ! Reclutar gente para una causa social es dificil: – toma tiempo – es tedioso – poco productivo. 4
  5. 5. Botivist:
 Usando Bots Para Reclutar Ciudadanos que Participen en Activismo
  6. 6. Botivist 6 Ciudadanos son reclutados para contribuir a una causa social. Causa Social Estrategia A Estrategia B Estrategia DEstrategia C Botivist
  7. 7. Estrategias de los Bots 7 Estrategia A: Directo Estrategia B: Solidaridad Estrategia C: Ganancia Colaboramos para combatir la corrupción? Cómo reducimos la corrupción en nuestras calles? Colaboramos para combatir la corrupción? Cómo reducimos la corrupción en nuestras calles? Hazlo por ti, por mi, por México! Colaboramos para combatir la corrupción? Cómo reducimos la corrupción en nuestras calles? Juntos podemos mejorar México! Estrategia D: Perdida Colaboramos para combatir la corrupción? Cómo reducimos la corrupción en nuestras calles? Sin colaboración, el futuro de México es negro! Usuario : CherryRex Contraseña: Pi3.14080878
  8. 8. Botivista: Funcionalidad 8 1. Identifica posibles voluntarios de Tweets que usan ciertas palabras claves 2. Bots mandan tweets a ciudadanos pidiendo acción inicial usando cada estrategia. Estrategia A Estrategia B Estrategia C 3. Bots ensamblan activismo con ciudadanos reclutados, guiándolos 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
  9. 9. Interacción Bots-Humanos 9 ! Bots evolucionan su relación con humanos mientras crece relación con el bot. ! Maquina de Estados
  10. 10. Botivist: User Study
  11. 11. Metodología: 
 Estudiando Participación Ciudadana 11 ! Cada bot interaactuó con grupos diferentes de ciudadanos. ! Estudiamos tipo de participación 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 había diferencia significativa entre estrategias. 

  12. 12. Resultados 12 *Bots que son directos generaron: ! Mayor número de respuestas de los ciudadanos. ! Mayor numero de voluntarios únicos. ! 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 deVoluntarios Numero de Interacciones Ciudadanas por Tweet del Bot Numero de Interacciones entre Ciudadanos
  13. 13. Metodología: 
 Estudiando Calidad de Contribuciones 13 ! Estudiamos calidad de participación ciudadana creada por cada estrategia. ! Analizamos: – La contribución 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 corrupción?
  14. 14. 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.
  15. 15. 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 

  16. 16. 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.
  17. 17. Qué diferencia tienen ciudadanos que contribuyen con bots vs ciudadanos que no? 17
  18. 18. Identificando Diferencias entre Ciudadanos 18 1.Tomamos todos los tweets personales de personas que bots tratamos de reclutar. 2. Usamos un Mann-Whitney rank 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 categoría encontrada.
  19. 19. 19 Categorias de Palabras Frequentes usadas por Ciudadanos
  20. 20. 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 política y noticias.
  21. 21. 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.
  22. 22. Backup Slides
  23. 23. 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 Work 23
  24. 24. Problem
  25. 25. 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
  26. 26. 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 Community Spreadable Media, H. Jenkins, et al. 
 "We the Media”, Dan Gilmor
  27. 27. 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,”
  28. 28. Some Consequences 28 Controversial designs. Non-useful tools. Limited Interactions.
  29. 29. 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. 30. . 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, ”
  31. 31. 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.
  32. 32. 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. 32 Jenkins, H. "Interactive audiences? The collective intelligence of media fans. Baym, N. et al., "Amateur experts International fan labour in Swedish independent music."
  33. 33. 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 Work 33
  34. 34. Engaging Online Audiences 34 ! I proposes two system designs to engage online audiences: (1) Authors understand in detail their audience and use that knowledge to engage and collaborate with them. (1) Authors don’t know anything about audience. Let bots to the work! ! Visualizing Audiences ! Automated Agents Person Visualizes+Understands! Use Knowledge to Engage! Let the bots do all the work ! Person understands audience in detail.
  35. 35. Visualizing Online Audiences Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  36. 36. Visualizing Online Audiences 36 • Long lists make it difficult to gauge 
 the traits of audience to motivate collaborations. Need for: – interfaces that facilitate understanding one’s audience to motivate support, collaborations and reciprocity. Published: COOP’15
  37. 37. What I propose: Hax 37
  38. 38. Design Proposals 38 ! Human in the loop interfaces to target audiences.
 ! Multifaceted data visualizations to help creators target audiences for their different collaborative tasks.
 ! Systems that let creators probe different strategies to recruit and call audiences to action.
  39. 39. Diversity Workflow 39 39 Interest Detection People’s Online Profiles User Modeling Input People’s Tweets Interest 1: Music Likes: Orange Interests:
 Bobby #yaMecanse5 liberen el peje! Interests Visualization Engine Interest: Pets Interest: Tech User Modeling Data Visualizations
  40. 40. 40 Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  41. 41. 41 Transparent Interface
  42. 42. 42 Social Awareness Interface
  43. 43. 43 Social Awareness Interface
  44. 44. 44 Social Awareness Interface
  45. 45. Hax Evaluation 45 Evaluation
  46. 46. Hax Evaluation Methodology 46 ! Between subjects study (N=15). Participants either used Hax or Facebook’s traditional interface to motivate audiences for a set of causes.
 ! Surveyed and interviewed participants on their experiences, strategies adopted to complete the tasks, benefits and drawbacks they saw, and a comparison ! between Hax/Facebook and other tools.

  47. 47. Hax Results 47 ! Participants preferred Hax over list-based interfaces. ! Participants identified Hax facilitated new interactions with audiences: – Serendipitous Discoveries – Facilitate Diffusion and Participation – Audience Diversity – AudienceVerification
  48. 48. 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 Work 48
  49. 49. Botivist
 Using Online Bots to Call Online Audiences to Action
  50. 50. Botivist: Calling Online Audiences to Action 50 ! Probes different strategies to recruit and initiate collaborations with online audiences. Solidarity Gain Loss Direct Could we collaborate to fight corruption?
 One for all, and all for one! Could we collaborate to fight corruption to help improve our cities?
 Could we collaborate to fight corruption? if not the future of our cities will be grim. Could we collaborate to fight corruption?
  51. 51. Methodology: Analyzing Audience Participation 51 ! Between subject study on Twitter to understand the type of audience participation each strategy generated. ! Analyzed: – number of replies – number of retweets, number of favorites – number of people participating. 
 ! Ran an anova test, and pairwise comparisons to see if there is significant difference between strategies.
  52. 52. Results 52 *Being Direct generated: ! the most replies from audiences; ! most unique number of volunteers; ! most number of interactions between audience members. ! Overview of the number of audience members and contributions which each strategy triggered. * ** * * Strategy *Showing Losses generated: ! high number of interactions among audience members. *Having Solidarity generated: ! Most number of retweets and favorites to bot’s content.
  53. 53. Results 53 ! 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.
  54. 54. Methodology: Most Active Audience Members 54 ! 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 

  55. 55. Results: Traits Most Active Audience Members. 55 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.
  56. 56. Engaging Online Audiences: Botivist
 Research Takeaways 56 ! Automated agents can be used to recruit online audiences, and incite collective efforts for an author’s cause. 
 ! Audiences react to strategies (gifts) differently when coming from humans or automated agents. 
 ! Specialized audiences engage more with specific strategies, e.g., solidarity. More general audience participate more with more general direct strategies.
  57. 57. Engaging Online Audiences
 Research Takeaways 57 ! Multifaceted data visualizations help authors identify strategies to motivate collaborations with their audience. ! Autonomous Agents help authors to probe strategies to motivate and start collaborations with their audience. 

  58. 58. 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. 58 Jenkins, H. "Interactive audiences? The collective intelligence of media fans. Baym, N. et al., "Amateur experts International fan labour in Swedish independent music."
  59. 59. 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 Work 59
  60. 60. Savage S., et al., I'm Feeling LoCo: A Location Based Context Aware Recommendation System, Lecture Notes in Geoinformation and Cartography. Springer. Engaging Online Audiences Opportunistically
  61. 61. Goals A tool that facilitates volunteering & contributing opportunistically: • Understands users’ lifestyle and preferences. • Understands users’ current context (activity) • Match tasks to available and interested users. 61
  62. 62. System Design
  63. 63. CROWDSOURCING
  64. 64. DAEMO: 
 SELF GOVERNED CROWD MARKET DAEMO: SELF-GOVERNED CROWD MARKET
  65. 65. DAEMO: 
 SELF GOVERNED CROWD MARKET BOOMERANG REPUTATION SYSTEM PROTOTYPE TASK
  66. 66. 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 Work 66
  67. 67. Online Audiences — Future Work
  68. 68. 68 Infrastructure to Study Online Collective Action Ecosystem • Scientific Framework to compare a collective effort’s results to how the effort was organized. • Visualizations to compare collective efforts across different axis. • Human-in-the-loop interfaces to correct, and incorporate external knowledge. • Allow scientific community to develop collective action principles.
  69. 69. 69 Theory of Design for Collective Action Systems • Study how interface designs (data visualizations, wearables) affect collaborations and computer based collective action. • Present design principles for collaborative and collective action systems.
  70. 70. 70 The Future Generation of Computer Collective Action • Study platforms that use big data to create end-to-end computer based collective action systems. Impacts: Smart Cities Healthcare Education Government and Non Profits Art
  71. 71. THANKS!
 INTERESTED IN JOINING THE RESEARCH? 
 Prof. Saiph Savage email: norma.savage@mail.wvu.edu
  72. 72. DAEMO: 
 SELF GOVERNED CROWD MARKET DAEMO: SELF-GOVERNED CROWD MARKET
  73. 73. DAEMO: 
 SELF GOVERNED CROWD MARKET BOOMERANG REPUTATION SYSTEM PROTOTYPE TASK
  74. 74. Backup Slides
  75. 75. Selected Publications 75 Understanding Online Audiences • Participatory Militias:An Analysis of an Armed Movement's Online Audience, CSCW’15 • Tag Me Maybe: Perceptions of Public Targeted Sharing on Facebook, HyperText’15 • The "Courage For” Facebook Pages:Advocacy Citizen Journalism in the Wild.
 C+J:Computation+Journalism Symposium 2014 • Online Social Persona Management, U.S. Patent Disclosure, filed, 2013
 
 System for Engaging Audiences • Botivism: Using Online Bots to CallVolunteers to Action, CSCW’16 (under review) • Visualizing Targeted Audiences, COOP’14 • I’m Feeling LoCo,A Location Based Context Aware Recommendation System, 
 Symposium of Location Based Services’11 • Socially and Contextually Appropriate Recommendation Systems,
 U.S. Patent Disclosure, filed 2014. • Search on the Cloud File System, PDCS’11: Parallel and Distributed Computing and Systems Conference • Traversing Data Using Data Relationships, U.S. Patent Disclosure, 
 filed 2012, published 2014. • CrowdsourcingVolunteers. Celebration of Women in Computing in Southern California 2014. • An Intelligent Environment for User Friendly Music Mixing,IE’12: International Conference on Intelligent Environments • Mmmmm:A multi-modal mobile music mixer,NIME’10: Conference on New Interfaces for • Musical Expression

  76. 76. Control Boomerang In Control condition 
 more workers are 
 rated as average.
  77. 77. 77 Evaluation Savage S., et al., I'm Feeling LoCo: A Location Based Context Aware Recommendation System, Lecture Notes in Geoinformation and Cartography. Springer. Launched our tool to the public. Study opportunistic participations that our tool facilitates. Interviews and surveys to understand subjective perceptions.
  78. 78. Understanding Online Audiences
 Research Takeaways 78 ! Friendly Environments
 -Authors & Audiences collaborate to distribute surprising content and overcome algorithms.
 -Authors tag as a strategy to harvest supportive audiences.
 • Controversial Environments
 -Authors use the strategy of adapting their self-presentation for their audience to encourage more participation.
 • Adverse Environments
 -Authors collaborate with their audience to produce news reports and even offline collective efforts. 
 -Authors have strategies to engage their audience: show solidarity by explaining how to keep safe, use offline events to recruit newcomers. 

  79. 79. Talk Outline ! Problem ! Thesis Contribution ! Understanding Online Audiences ! Friendly:Tag me Maybe ! Controversial:Author Self Presentation and Audience Participation ! Adverse: Participatory Militias ! Engaging Online Audiences ! Visualizing Online Audiences ! Botivist ! Online Audiences — Future Work 79
  80. 80. Tag Me Maybe : Integrating the Audience into Content 80Published: Hypertext’15 Tags
  81. 81. Method 81 Study Participants are interviewed and surveyed Lessons Learned Results Responses are Categorized and Quantified • Interview Questions focused on people’s perspectives on publicly tagging friends in Facebook posts from the view of: – Content Producers (Taggers) – Audiences Tagged (Taggees) – Passive Audiences (Viewers) • Conducted qualitative coding to categorize interview responses.
  82. 82. Demographics Participants 82 Total number of participants 270 Participants recruited from FB 32 Participants recruited outdoors 88 Participants recruited from Amazon Mechanical Turk 150 Sex Demographics 43% Female, 57% Male Age Demographics 18-68 years old, median age of 22
  83. 83. Results 83 Percentage of Participants who Referenced each Perspective
  84. 84. Understanding Online Audiences: 
 Friendly Environments
 84 ! Content Producers tag as a strategy to harvest supportive audiences. ! Content Producers & Audiences collaborate to distribute surprising content and overcome algorithms.
  85. 85. Content Producer’s Self Presentation and Audience Participation 85 { Person’s Post Self Presentation (Implicit Interests) {Person’s Profile Self Presentation (Explicit Interests) Patents Intel Labs
  86. 86. Method 86 1. Detect different ways content producers present themselves in a controversial community. 2. Compare Self-Presentation with content popularity based on number of comments. 3. Interview participants from each cluster to further understand the behavior. Input Topic Modeling People’s PostsPeople’s Profiles User Modeling Clustering } Different types of self presentations
  87. 87. LiveJournal Data 87 ! Collected LiveJournal (LJ) Posts from ONTDP and Profiles from all bloggers to ONTDP from March 30th 2012 to July 11th 2012.
 Authors 296 Commenters 1,972 Interviewees 12 LJ posts 1,200 Comments 30,934 Profile Tags 9,812 Post Tags 1,622
  88. 88. Results 88 Content producer’s whose self presentations were tailored to the audience received the most attention and participation from the audience. People who got most comments from their audience.
  89. 89. Understanding Online Audiences: 
 Controversial Environments
 89 ! Content Producers use the strategy of adapting their self-presentation to audience’s interests to obtain more participation.

  90. 90. Participatory Militias 90 Armed civilian forces have successfully fought back against organized crime.
 These groups have been active on social media, particularly on Facebook pages titled “Courage for X Region” to inform and recruit citizens to resist the criminals. Published: CSCW’15
  91. 91. Data “Courage For” Facebook Posts: 25,878 Fans: 488,029 Comments:108,967 Post Likes: 1,481,008 Reshares: 364,660
  92. 92. Goal Use the “Courage For” pages as a lens to understand 1) the type of online content shared by content creators in adverse scenarios and how the online audience engages with content creators; 2) characteristics of the most active audience members.
  93. 93. Methodology Topic Understanding 93 ! We used a grounded theory approach to identify the main topics inVXM’s posts. 1. Extracted topics from set of 700 randomly selected posts. 2. Used oDesk to hire three Spanish-speaking, college educated people to categorize theVXM posts. 3. We used a majority rule to determine the topic each post. Topic 4 Topic 2
  94. 94. What do Content Producers Share? 94 ! Primarily News Reports, but also content to keep audience safe and mourn their personal losses. News Reports: Propaganda: Online Safety: Obituaries/Missing Persons:
  95. 95. How does the audience engage in adverse scenarios? 95 ! The most popular public figures were not necessarily ones most mentioned by the Content Producers. ! Different Spikes in Audience's posting and Content Producers’.
  96. 96. Attributes Most Active Audience 96 ! We discover common traits of the most active audience members. Identify highly active 
 audience members. Characterize highly active audience 
 Use Mean Shift to cluster active audience 

  97. 97. What are the traits of the most active audience members? 97 Drug Cartel Savvy • Majority of comments about the drug cartels. • Produced the most and longest comments. • 33% of the most active. • Most references to locations. Geographers • No reference to any public figures but did reference geographical locations. • Produced the second-most and longest comments. • 1% of the most active. Government Gossipers • Produced the least comments and shortest. • 66% of the most active. • Majority of comments were about the Government. • Some practiced redundancy in their comments.
  98. 98. Understanding Online Audiences
 Research Findings 98 ! Friendly Environments
 -Authors & Audiences collaborate to distribute surprising content and overcome algorithms.
 -Authors tag as a strategy to harvest supportive audiences.
 • Controversial Environments
 -Authors use the strategy of adapting their self-presentation to their audience to obtain more participation. • Adverse Environments
 -Authors collaborate with their audience to produce news reports and even offline collective efforts. 
 -Authors have strategies to engage their audience: show solidarity by explaining how to keep safe, use offline events to recruit newcomers. 
 -Audience empowered to drive the narrative of events. • 

  99. 99. Research Takeaways Understanding Audiences 99 ! Content Authors and Online Audiences collaborate to produce collective efforts. • Content Authors use different strategies to harvest supportive audiences. ! Relationship between Content Producers and Online Audiences resembles Gift Economy.
  100. 100. R2: How does the audience engage in adverse scenarios? 100 ! Offline events most effective to recruit new participants to page . ! Most popular content created during major offline events.
  101. 101. Integrating the Audience in a Post: People Tagging in Posts Tagees receive Content shared with Shared with tagees’ friends & People Tagged in Post Tag 101
  102. 102. Engaging Online Audiences 102 ! I proposes two system designs to engage online audiences: (1) Authors visualize the characteristics of their audience (2) Authors use knowledge to decide themselves strategy to engage audience & start collaborations (1) Authors send out automated agents that try strategies to engage audience & start collaborations ! Visualizing Audiences ! Automated Agents
  103. 103. Research Takeaways Understanding Audiences 103 • Content Authors use different strategies to harvest supportive audiences. ! Content Authors and Online Audiences collaborate to produce collective efforts. ! Relationship between Content Producers and Online Audiences resembles Gift Economy.
  104. 104. Research Takeaways Engaging Audiences 104 ! Multifaceted DataVisualizations help content producers to better identify strategies (gifts) to motivate collaborations with their audience. ! Autonomous Agents help content producers to probe strategies to motivate collaborations with their audience.
  105. 105. Engaging Online Audiences 105 ! I propose two system designs to engage online audiences in gift economy: (1) Content producers visualize the characteristics of their audience (2) Content producers use knowledge to decide themselves strategy to engage audience & start collaborations (1) Authors send out automated agents that try strategies to engage audience & start collaborations ! Visualizing Audiences ! Automated Agents
  106. 106. Study Participants are interviewed and surveyed Lessons Learned Results Responses are Categorized and Quantified Study Design 106
  107. 107. Total number of participants 270 Participants recruited from FB 32 Participants recruited outdoors 88 Participants recruited from Amazon Mechanical Turk 150 Sex Demographics 43% Female, 57% Male Age Demographics 18-68 years old, median age of 22 Participant Demographics 107
  108. 108. Can Online Bots be used to engage online audiences for a content producer’s cause?
  109. 109. Results Percentage of Participants who referenced each perspective 109 0 10 20 30 40 50 Percentage 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 10 Sharing Spamminess Relationships Identity Indifference Reminders Self-Indulgence
  110. 110. Results Percentage of Participants who referenced each perspective 110Taggers Taggees Viewers 0 10 20 30 40 50 60Percentage 1 2 3 0 1 2 3 4 5 6 7 8 9 10 Spamminess Identity Maintain relationships Sharing Indifference Self−indulgence Generate reminders 1 2 3 0 1 2 3 4 5 6 7 8 9 10 Spamminess Identity Maintain relationships Sharing Indifference Self−indulgence Generate reminders
  111. 111. Takeaways • Need for spaces where people can collaborate to design their online image and distribute meaningful content. • Need for spaces to organize large audiences for real world activities • People assume roles. • Need for spaces where people can lend their identity for a collaboration. • People want to reach and meet new strange audiences. Need to provide them with the bridges. • People have to invest time in manually learning about their audiences. 111
  112. 112. Botivist: 
 Using Online Bots to call Audiences to Action 112
  113. 113. Engaging Online Audiences 113 ! I propose two system designs to engage online audiences in gift economy: (1) Authors visualize the characteristics of their audience (2) Authors use knowledge to decide themselves strategy to engage audience & start collaborations (1) Authors send out automated agents that try strategies to engage audience & start collaborations ! Visualizing Audiences ! Automated Agents
  114. 114. R3: What are the traits of the most active audience members in adverse scenarios?
  115. 115. Results Frequencies Participants reported to being: a) taggers; b) tagees; c) viewers. 115Taggers Taggees Viewers 0 10 20 30 40 50 Percentage 1 2 3 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Nothing Little Somewhat Much
  116. 116. Results How much Participants reported to enjoying being: a) taggers; b) tagees; c) viewers. 116 Taggers Taggees Viewers 0 10 20 30 40 50Percentage 1 2 3 −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Not at all Very little Somewhat To a great extent
  117. 117. Takeaways • Audiences in general enjoy greatly being involved in content. • Content creators in general enjoy the interaction somewhat, likely due to stress of integrating people in uninteresting/ inappropriate content. 117
  118. 118. Understanding Online Audiences
 Research Takeaways 118 ! Content Authors and Online Audiences collaborate to produce collective efforts.
 • Content Authors use different strategies to recruit and engage supportive audiences.
  119. 119. What type of self-presentations get more responses from an audience? {Person’s Post Self Presentation (Implicit Interests) {Person’s Profile Self Presentation (Explicit Interests) 119
  120. 120. R2: How does the Audience engage in Adverse Scenarios?
  121. 121. Workflow Topic Modeling People’s PostsPeople’s Profiles User Modeling Clustering } Input 121
  122. 122. R3: What are the traits of the most active audience members? Geographers • No reference to any public figures but did reference geographical locations. • Produced the second-most and longest comments. • 1% of the most active.
  123. 123. R3: What are the traits of the most active audience members? Government Gossipers • Produced the least comments and shortest. • 66% of the most active. • Majority of comments were about the Government. • Some practiced redundancy in their comments.
  124. 124. LiveJournal Data • 
 Collected LiveJournal (LJ) Posts from ONTDP and Profiles from all bloggers to ONTDP from March 30th 2012 to July 11th 2012.
 
 Authors 296 Commenters 1,972 Interviewees 12 LJ posts 1,200 Comments 30,934 Profile Tags 9,812 Post Tags 1,622124
  125. 125. Results 125 Most popular self presentation assumed by people is content hunter.
  126. 126. Results • People who tried to match profiles with posts were less in touch with audience: got less comments. 126 People who got most comments from their audience.
  127. 127. Takeaways • Support Author’s and Audience’s Role Play. • Help Authors and Audiences visualize the interests and role play of others. • Need for Socially Aware Presentation Cards. 127
  128. 128. Talk Outline • Integrating the Audience (People Tagging) • Self Presentation and Audience Participation • Tools for Targeting Audiences • Audiences in Adverse Scenarios Friendly Adverse 128
  129. 129. R1: What do Content Producers Share with their Audience in Adverse Scenarios?
  130. 130. Participatory Media Content Distribution 130 Community Audience Reporter Publisher Reporter Advertiser Editor Community Spreadable Media, H. Jenkins, et al. "We the Media”, Dan Gilmor Emerging Relationships, Tensions, Experiences … are Unclear!
  131. 131. 131 ! Online audiences engage with content similar to players in Alternate Reality games and fandoms. ! Relationship between content producers and online audiences resembles Gift Economy. Understanding Online Audiences

  132. 132. Participatory Media Ecosystem 132 Blogsphere: the emerging Media Ecosystem by
 John Hilter, Microcontent News Content Producers Radio,TV, Newspaper, UsersSources Story Ideas Story Iterations Conversation Audience Audience
  133. 133. Many tools to analyze audiences … but useful to engage?
  134. 134. Hard to Design Engaging Tools 134 ! Most platforms are adaptations of traditional marketing tools, ignoring emerging dynamics. *R. Zamora, Individual Report on ‘‘Audience Interactivity and Participation’’, *A. Bergström,Audience Interactivity and Participation, 2012. Tools measure the wrong things Use one size fits all reporting. Most ignore text analytics. User needs to act as detective!
  135. 135. Understanding Online Audiences
 Research Takeaways 135 ! Creators & Audiences struggle with algorithmic filtering. ! Creators & Audiences collaborate to overcome algorithms. ! Creators study their audiences to harvest support for different collaborations. ! Creators probe different strategies to harvest supportive audiences for collaborations. ! Audience empowered to define collectively a narrative without following author lead.
  136. 136. Challenges 136 ! Relationships between audiences and authors is unclear ! Can be hard to design engaging relevant tools.
  137. 137. Understanding Online Audiences
 Research Takeaways 137 ! Authors and Online Audiences interact with each other to produce collective efforts. ! Authors use different strategies to recruit and engage with supportive audiences.
  138. 138. Understanding Online Audiences
 138 Research Contributions ! Creators & Audiences decide to collaborate to popularize content, overcoming sometimes even algorithmic filtering. ! Creators study their audiences to identify strategies to harvest support for different collaborations. ! Creators probe different strategies to harvest supportive audiences for collaborations. ! Audience empowered to define collectively a narrative without following author lead.
  139. 139. 139 Previous research considers that information becomes “viral”. Removing decision from people. Understanding Online Audiences
  140. 140. Understanding Online Audiences
 140 Research Findings ! Creators & Audiences decide to collaborate to popularize content, overcoming sometimes even algorithmic filtering. ! Creators study their audiences to identify strategies to harvest support for different collaborations. ! Creators probe different strategies to harvest supportive audiences for collaborations. ! Audience empowered to define collectively a narrative without following author lead.
  141. 141. Traditional Media 141 ! Only select persons are authors ! One-way non reciprocal communication.
  142. 142. Participatory Media 142 ! Anyone can be an author ! Audiences can participate and interact.
  143. 143. Understanding Online Audiences 143Friendly Adverse ! I use social media to understand the experiences, relationships, tensions, and interactions emerging from content producers and their online audience. Burbank's (1967) Interactive audience space, Berkenkotter C (1981) Understanding a writer’s awareness of audience
  144. 144. Impact of this work ! This research expands our understanding of audiences and content producers in wider spectrum.
 ! Shifts design of tools to engage online audiences from market economy to gift economy. 144 Jenkins, H. "Interactive audiences? The collective intelligence of media fans. Baym, N. et al., "Amateur experts International fan labour in Swedish independent music."
  145. 145. Botivist
 Using Online Bots to Call Online Audiences to Action
  146. 146. Engaging Online Audiences 146 ! I use the understanding to design novel tools that help authors to better engage with their online audience.
  147. 147. Engaging Online Audiences
 Design Proposals 147 ! Human in the loop interfaces to target audiences. ! Multifaceted data visualizations to help creators target audiences for their different collaborative tasks. ! Systems that let creators probe different strategies to recruit and call audiences to action.
  148. 148. 148 Research Contributions ! Analyses of Online Audiences ! Qualitative and quantitative analysis of audience targeting mechanisms (Hypertext 2015) ! Analysis of content producer self-presentation and audience engagement (work led to patent submissions with Intel) ! Design and Evaluation of Tools for Engaging Online Audiences. ! [Hax] ! [CSCW submission]
  149. 149. Visualizing Collaboration Opportunities Supporting Online Audiences Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  150. 150. (1) Authors visualize the characteristics of their audience (2) Authors use knowledge to decide themselves strategy to engage audience & start collaborations (1) Authors send out automated agents that try strategies to engage audience & start collaborations ! Visualizing Audiences ! Automated Agents
  151. 151. Advertisers Media Organization Web sites
 TV Shows
 News Papers Audience A) B) Community Audience Reporter Publisher Reporter Advertiser Editor Community
  152. 152. Community Audience Reporter Publisher Reporter Advertiser Editor Community
  153. 153. Friendly-Intimate Spaces Adverse Spaces Controversial Spaces
  154. 154. Challenges 154 ! Difficult to design engaging media. *Pasquali, N,“Emerging Topics in the Research on Digital Audiences and Participation” *H. Sanchez Gonzales, Connectivity between the Audience and the Journalist Source: Mashable
  155. 155. Understanding Online Audiences (Adverse) Savage S., Monroy-Hernandez A., Participatory Militia, CSCW’15 Participatory Militias
 An Analysis of an Armed Movement’s Online
  156. 156. Background Armed civilian forces have successfully fought back against the criminals in the region.
  157. 157. Goal Use the “Courage For” pages as a lens to understand 1) the type of online content shared by content creators in adverse scenarios; 2) how the online audience engages with content creators; 3) characteristics of the most active audience members.
  158. 158. 158 Problems Current Tools • Long lists make it 
 difficult to gauge 
 the traits of their 
 audience
  159. 159. Hax Goals An interface that facilitates visualizing collaboration opportunities • Visualizing recruitment opportunities by identifying interested audiences. • Visualizing opportunity structures (bridges to interested audiences) • Visualizing collaboration opportunities for collective action (offline and online) 159
  160. 160. 160 Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  161. 161. 161 Workflow Interest Detection People’s Online Profiles User Modeling Person’s Search Query Input People’s Online Profiles Interest 1: Music Interest 2: Pets Interest 3: Technology 
Carly Likes: Orange Interests:
 Bobby Likes: Lady Gaga,Mac book Pro Interests Musi Craft Technolo Pets Craft Recommendation + Visualization Engine
  162. 162. 162 Transparent Interface Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  163. 163. 163 Social Awareness Interface Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  164. 164. 164 Social Awareness Interface Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  165. 165. 165 Social Awareness Interface Savage S., et al.,Visualizing Targeted Online Audiences,
 COOP’14: Conference on the Design of Cooperative Systems.
  166. 166. 166 Evaluation Launched our tool to the public. Study type of audience recruitment for which our tool is used, and successes. Interviews and surveys to understand subjective perceptions.
  167. 167. 167 Results Multi-modal visualizations facilitated: -Serendipitous Discoveries -Visualizing Other People’s Likelihood of Participation (Geo & Knowledge-based) -Visualizing Diffusion (Spread Information) -Audience Diversity (Cultural and Tastes) -Audience Verification
  168. 168. 168 Outreach: Largest Scale Latina Hackathon
  169. 169. • Context Aware Systems for Opportunistic Participations Supporting Online Audiences Savage S., et al., I'm Feeling LoCo: A Location Based Context Aware Recommendation System, Lecture Notes in Geoinformation and Cartography. Springer.
  170. 170. Goals A tool that facilitates volunteering & contributing opportunistically: • Understands users’ lifestyle and preferences. • Understands users’ current context (activity) • Match tasks to available and interested users. 170
  171. 171. 171 System Design Savage S., et al., I'm Feeling LoCo: A Location Based Context Aware Recommendation System, Lecture Notes in Geoinformation and Cartography. Springer.
  172. 172. 172 Evaluation Savage S., et al., I'm Feeling LoCo: A Location Based Context Aware Recommendation System, Lecture Notes in Geoinformation and Cartography. Springer. Launched our tool to the public. Study opportunistic participations that our tool facilitates. Interviews and surveys to understand subjective perceptions.
  173. 173. We identified posts that referenced the public figures and organizations involved in the conflict. Content Analysis: Public Figures 1. Collected Wikipedia & Proceso 
 articles related to conflict in the region 2. Identified all proper names. 3. Add or merge alternate names for 
 each public figure. “Estanislao Beltran”, “Papa Smurf”,
 “ Estanislao” Public Figure 1 Public Figures 1,2 Public Figure 5 Public Figure 3 Public Figure 1 Public Figure 1 4. Identified VXM posts and comments that mentioned each public figure.
  174. 174. Related On-Going Projects • Crowdsourcing 
 volunteer tasks 174 • Social Crowd Controlled Orchestra
  175. 175. Grounded Theory StagePurpose Codes: Identifying anchors that allow the key points of the data to be gathered Concepts: Collections of codes of similar content that allows the data to be grouped Categories: Broad groups of similar concepts that are used to generate a theory Theory: A collection of explanations that explain the subject of the research
  176. 176. Related Work Self-Presentations to Audiences • I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience, 2011, Marwick A. , boyd D. • Managing impressions online: Self‐presentation processes in the online dating environment, 2006, N Ellison, R Heino, J GibbsPal, A., and Counts, S. 2011. What’s in a @name? How name value biases judgement of microblog authors A familiar face(book): profile elements as signals in an online social network, 2007, Lampe, C.; Ellison, N.; and Steinfield, C. Lampe, C.; Ellison, N.; and Steinfield, C. A familiar face(book): profile elements as  signals in an online social network. Mining Analytics Mining expertise and interests from social Collective Intelligent Augmenting Human Intellect, 1962, Engelbart Douglas, Collective Intelligence: Mankind's Emerging World in Cyberspace. 1999, Pierre Levy.
  177. 177. Related Work in Author’ Self Presentation and Audience 177 • Marwick A., boyd d., I tweet honestly, I tweet 
 passionately: Twitter users, 
 context collapse, and the imagined audience, 2011, New Media and Soci • Ellison N, Heino R, Gibbs J, Managing 
 Impressions online: self presentation processes in the online dating enviro 2006, Journal of Computer‐Mediated Communication • Lampe, C.; Ellison, N, and Steinfield, C, A familiar 
 face(book): profile elements as signals in an online social network, 2007,

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