Crowdsourcing Activism

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.

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 estratégicamente es complicado: requieres

personal con mucha experiencia.

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

Botivist: Usando Bots Para Reclutar Ciudadanos que Participen en Activismo

Botivist

Ciudadanos son reclutados para contribuir a una causa social.

Causa Social

Estrategia A Estrategia BEstrategia DEstrategia C

Botivist

Estrategias de los Bots

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: PerdidaColaboramos para combatir la corrupción? Cómo reducimos la corrupción en nuestras calles? Sin colaboración, el futuro de México es negro!

Usuario : CherryRexContraseña: Pi3.14080878

Botivista: Funcionalidad

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

Interacción Bots-Humanos

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

! Maquina de Estados

Botivist: User Study

Metodología: Estudiando Participación Ciudadana

! 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.

Resultados

*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 de Voluntarios

Numero de Interacciones Ciudadanas por Tweet del Bot

Numero de Interacciones entre Ciudadanos

Metodología: Estudiando Calidad de Contribuciones

! 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?

Results

! 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

!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.

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.

Qué diferencia tienen ciudadanos que contribuyen con bots vs ciudadanos que no?

Identificando Diferencias entre Ciudadanos

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 categoría encontrada.

Categorias de Palabras Frequentes usadas por Ciudadanos

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.

Botivist Research Takeaways

! 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

! 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

! 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

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

Controversial designs.Non-useful tools.Limited Interactions.

Example: Problematic Design

Facebook gets sued!

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

User A Company

User B receives sponsored story

! 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

! 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

Engaging Online Audiences

! 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.

Visualizing Online Audiences

Savage S., et al.,Visualizing Targeted Online Audiences,COOP’14: Conference on the Design of Cooperative Systems.

Visualizing Online Audiences

• 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

What I propose: Hax

Design Proposals

! 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.

Diversity Workflow

Interest Detection

People’s Online Profiles

User Modeling

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

Transparent Interface

Social Awareness Interface

Hax Evaluation

Evaluation

Hax Evaluation Methodology

! 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.

Hax Results

! Participants preferred Hax over list-based interfaces.! Participants identified Hax facilitated new interactions with audiences:– Serendipitous Discoveries– Facilitate Diffusion and Participation– Audience Diversity– Audience Verification

Botivist Using Online Bots to Call Online Audiences to Action

Botivist: Calling Online Audiences to Action

! Probes different strategies to recruit and initiate collaborations with online audiences.

Solidarity

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?

Methodology: Analyzing Audience Participation

! 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.

Results

*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.

Results

! 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

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

Characterize highly Use Mean Shift to cluster active

audience

Results: Traits Most Active Audience Members.

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.

Engaging Online Audiences: Botivist Research Takeaways

! 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.

Engaging Online Audiences Research Takeaways

! 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.

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.

58Jenkins, H. "Interactive audiences? The collective intelligence of media fans.Baym, N. et al., "Amateur experts International fan labour in Swedish independent music."

!Online Audiences — Future Work

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

A tool that facilitates volunteering & contributing opportunistically:

• Understands users’ lifestyle and preferences.• Understands users’ current context (activity)• Match tasks to available and interested users.

System Design

CROWDSOURCING

DAEMO: SELF GOVERNED CROWD MARKET

DAEMO: SELF-GOVERNED CROWD MARKET

BOOMERANG REPUTATION SYSTEM PROTOTYPE TASK

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

Online Audiences — Future Work

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.

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.

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 CitiesHealthcareEducationGovernment and Non ProfitsArt

THANKS!INTERESTED IN JOINING THE RESEARCH? Prof. Saiph Savage email: norma.savage@mail.wvu.edu

DAEMO: SELF-GOVERNED CROWD MARKET

BOOMERANG REPUTATION SYSTEM

PROTOTYPE TASK

Backup Slides

Selected Publications

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 Call Volunteers 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.• Crowdsourcing Volunteers. 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

Control Boomerang

In Control condition more workers are rated as average.

Evaluation

Launched our tool to the public. Study opportunistic participations that our tool facilitates. Interviews and surveys to understand subjective perceptions.

Understanding Online Audiences Research Takeaways

! 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.

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

Tag Me Maybe : Integrating the Audience into Content

80Published: Hypertext’15

Method

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.

Demographics Participants

Total number of participants 270

Participants recruited from FB 32

Participants recruited outdoors 88

Participants recruited from Amazon Mechanical Turk

Sex Demographics 43% Female, 57% Male

Age Demographics 18-68 years old, median age of 22

Results

Percentage of Participants who Referenced each

Perspective

Understanding Online Audiences: Friendly Environments

! Content Producers tag as a strategy to harvest supportive audiences.

! Content Producers & Audiences collaborate to distribute surprising content and overcome algorithms.

Content Producer’s Self Presentation and Audience Participation

{Person’s Post Self

Presentation(Implicit Interests)

{Person’s Profile Self

Presentation (Explicit Interests)

Patents Intel Labs

Method

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.

Topic Modeling

People’s PostsPeople’s Profiles

User Modeling

Clustering

Different types of self presentations

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 1,622

Results

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.

Understanding Online Audiences: Controversial Environments

! Content Producers use the strategy of adapting their self-presentation to audience’s interests to obtain more participation.

Participatory Militias

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

Data “Courage For” Facebook

Posts: 25,878 Fans: 488,029 Comments:108,967 Post Likes: 1,481,008Reshares: 364,660

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.

Methodology Topic Understanding

!We used a grounded theory approach to identify the main topics in VXM’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 the VXM posts. 3. We used a majority rule to determine the topic each post.

Topic 4

Topic 2

What do Content Producers Share?

! Primarily News Reports, but also content to keep audience safe and mourn their personal losses.

News Reports: Propaganda:

Online Safety: Obituaries/Missing Persons:

How does the audience engage in adverse scenarios?

! The most popular public figures were not necessarily ones most mentioned by the Content Producers.

! Different Spikes in Audience's posting and Content Producers’.

Attributes Most Active Audience

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

Characterize highly active audience

Use Mean Shift to cluster active audience

What are the traits of the most active audience members?

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.

Understanding Online Audiences Research Findings

! 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.

Research Takeaways Understanding Audiences

! 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.

R2: How does the audience engage in adverse scenarios?

! Offline events most effective to recruit new participants to page .

! Most popular content created during major offline events.

Integrating the Audience in a Post: People Tagging in

Tagees receive Content shared with

Shared with tagees’ friends &

People Tagged in PostTag

! 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

Research Takeaways Understanding Audiences

• Content Authors use different strategies to harvest supportive audiences.

! Relationship between Content Producers and Online Audiences resembles Gift Economy.

Research Takeaways Engaging Audiences

! Multifaceted Data Visualizations 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.

! 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

Participants are interviewed

and surveyed Lessons Learned

Results

Responses are Categorized and

Quantified

Study Design

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

Can Online Bots be used to engage online audiences for a content producer’s cause?

ResultsPercentage of Participants who referenced each

perspective

Student Version of MATLAB

1 2 3 4 5 6 70

SharingSpamminessRelationshipsIdentityIndifferenceRemindersSelf-Indulgence

ResultsPercentage of Participants who referenced each

perspective

110Taggers Taggees Viewers0

1 2 30

SpamminessIdentityMaintain relationshipsSharingIndifferenceSelf−indulgenceGenerate reminders

1 2 30

SpamminessIdentityMaintain relationshipsSharingIndifferenceSelf−indulgenceGenerate reminders

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.

Botivist: Using Online Bots to call Audiences to Action

! I propose two system designs to engage online audiences in gift economy:

R3: What are the traits of the most active audience members in adverse scenarios?

ResultsFrequencies Participants reported to being:

a) taggers; b) tagees; c) viewers.

115Taggers Taggees Viewers0

1 2 3−1

−0.8

−0.6

−0.4

−0.2

NothingLittleSomewhatMuch

ResultsHow much Participants reported to enjoying being:

a) taggers; b) tagees; c) viewers.

116Taggers Taggees Viewers

1 2 3−1

−0.8

−0.6

−0.4

−0.2

Not at allVery littleSomewhatTo a great extent

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.

• Content Authors use different strategies to recruit and engage supportive audiences.

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)

R2: How does the Audience engage in Adverse Scenarios?

Workflow

Topic Modeling

People’s PostsPeople’s Profiles

User Modeling

Clustering

} Input

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.

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.

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

Results

Most popular self presentation assumed by people is content hunter.

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.

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.

Talk Outline

• Integrating the Audience (People Tagging)

• Self Presentation and Audience Participation

• Tools for Targeting Audiences

• Audiences in Adverse Scenarios

Friendly

Adverse

R1: What do Content Producers Share with their Audience in Adverse Scenarios?

Participatory Media Content Distribution

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!

! 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

Participatory Media Ecosystem

Blogsphere: the emerging Media Ecosystem byJohn Hilter, Microcontent News

Content Producers Radio, TV, Newspaper, UsersSources

Story Ideas

Story Iterations

Conversation

Audience

Many tools to analyze audiences … but useful to engage?

Hard to Design Engaging Tools

! 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 thingsUse one size fits all reporting.Most ignore text analytics.User needs to act as detective!

! 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.

Challenges

! Relationships between audiences and authors is unclear! Can be hard to design engaging relevant tools.

! Authors and Online Audiences interact with each other to produce collective efforts.

! Authors use different strategies to recruit and engage with supportive audiences.

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.

Previous research considers that information becomes “viral”. Removing decision from people.

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.

Traditional Media

! Only select persons are authors! One-way non reciprocal communication.

Participatory Media

! Anyone can be an author! Audiences can participate and interact.

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

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.

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

Botivist Using Online Bots to Call Online Audiences to Action

! I use the understanding to design novel tools that help authors to better engage with their online audience.

Engaging Online Audiences Design Proposals

! 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.

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]

Visualizing Collaboration Opportunities

Supporting Online Audiences

Advertisers

Media Organization

Web sites TV ShowsNews Papers

Audience

Community

Audience

Reporter Publisher

Reporter

Advertiser

Editor

Community

Audience

Reporter Publisher

Reporter

Advertiser

Editor

Community

Friendly-Intimate Spaces

Adverse Spaces

Controversial Spaces

Challenges

! 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

Understanding Online Audiences (Adverse)

Savage S., Monroy-Hernandez A., Participatory Militia, CSCW’15

Participatory Militias An Analysis of an Armed Movement’s Online

Background

Armed civilian forces have successfully fought back against the criminals in the region.

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.

Problems Current Tools

• Long lists make it difficult to gauge the traits of their audience

Hax GoalsAn 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)

Workflow

Interest Detection

User Modeling

Person’s Search Query

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

Transparent Interface

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.

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

Outreach: Largest Scale Latina Hackathon

• Context Aware Systems for Opportunistic Participations

Supporting Online Audiences

A tool that facilitates volunteering & contributing opportunistically:

• Understands users’ lifestyle and preferences.

• Understands users’ current context (activity)

• Match tasks to available and interested users.

System Design

Evaluation

Launched our tool to the public. Study opportunistic participations that our tool facilitates. Interviews and surveys to understand subjective perceptions.

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 1,2Public Figure 5

Public Figure 3

Public Figure 1

4. Identified VXM posts and comments that

mentioned each public figure.

Related On-Going Projects

• Crowdsourcing volunteer tasks

• Social Crowd Controlled Orchestra

Grounded TheoryStage Purpose

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

Related WorkSelf-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 Gibbs

Pal, A., and Counts, S. 2011. What’s in a @name? How name value biases judgement ofmicroblog 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 IntelligentAugmenting Human Intellect, 1962, Engelbart Douglas,

Collective Intelligence: Mankind's Emerging World in Cyberspace. 1999, Pierre Levy.

Related Work in Author’ Self Presentation and Audience

• Marwick A., boyd d., I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience, 2011, New Media and Society

• Ellison N, Heino R, Gibbs J, Managing Impressions online: self presentation processes in the online dating environment, 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, CHI

Crowdsourcing Activism

Social Media

Transcript of Crowdsourcing Activism

Judicial Activism and Religion-Based Tensions in India and Israel

Crowdsourcing José Antonio Gallego

El escenario de crowdsourcing

Introducción al crowdsourcing

Crowdsourcing industrial 30 oct 2

¿Qué es crowdsourcing?

Crowdsourcing en el periodismo

Aplicación del crowdsourcing en contextos educativos

El Arte del Crowdsourcing

Crowdsourcing, innovacion abierta

Innovación participatica crowdsourcing

Online Activism: PDF Europe

Estrategia s Del Activism Oen Facebook

Con Crowdsourcing

Crowdsourcing y la Innovación

Crowdsourcing y crowdfunding

Crowdsourcing - Brainfood Abril, 2014 - McCANN

CROWDSOURCING, compilación

Innovacion crowdsourcing - AIESEC Young Lions

Facebook, Twitter y Crowdsourcing.