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Data Portraits and Intermediary
Topics: Encouraging Exploration
of Politically Diverse Profiles
Eduardo Graells-Garrido*
T...
This is a discussion network
about abortion, in Chile, on
Twitter.
Purple nodes are pro-choice
users, and Green are pro-li...
Is it possible to encourage
exploration and acceptance of user
profiles recommended on the basis
of political diversity?
I...
Previous Approaches
Previously, many paths have been pursued to make people
explore diverse information / connect with oth...
But... users do not value diversity.
Previous approaches have been direct:
“user is biased, here is a system to be unbiase...
How to recommend people with diverse views?
Using homophily itself to improve first
impressions
with intermediary topics t...
Intermediary Topics using LDA
Intermediary topics are LDA topics
estimated on a user corpus with
weighted information cent...
In contrast with “physical world” portraits, here the user generated content is
abstracted to build a portrait.
http://per...
Our Platform
Present a data portrait of Twitter users, and use it as a context to include
recommendations of people that a...
Design focused on self-
image projection.
Recommendations are
displayed using Circle
Packing and generated
using Intermediary
Topics.
They are clustered
according t...
Implementation and Evaluation
We want to measure whether users engaged with recommendations
or not:
- How much do they exp...
“In the Wild”
The platform in http://auroratwittera.cl is open
for registration in an uncontrolled setting.
When users sig...
Algorithmic conditions
- Baseline: only recommend people ranked by their distance using Kullback-
Leibler Distance over to...
Experimental Setup
Between-Subjects
Negative Binomial and Logistic
Regressions
The model includes interaction
terms
N = 12...
Interpretation of Results
The joint interaction of visualization (circle pack) and intermediary topics allows
politically-...
Conclusions
Indirect approaches have potential.
Users might not want to change (improve) their behavior. This should be us...
Thank you! Do you have any questions?
Contact Us:
eduardo.graells@telefonica.com / @carnby
Reproduce Our Experiments!
The ...
When users connected their
accounts, the system notified
them:
1) when the portrait was ready for
the first time.
2) when ...
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Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

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In micro-blogging platforms, people connect and interact with others. \ However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. \ Many efforts to make people connect with those who think differently have not worked well. \ In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. \ We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. \ It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. \ We performed an ``in the wild'' evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. \ Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.

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Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

  1. 1. Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles Eduardo Graells-Garrido* Telefónica R&D, Chile Mounia Lalmas Yahoo, UK Ricardo Baeza-Yates UPF, Catalonia / Univ. of Chile
  2. 2. This is a discussion network about abortion, in Chile, on Twitter. Purple nodes are pro-choice users, and Green are pro-life users. This behavior happens because of Homophily, a cognitive bias that makes people connect mostly with like-minded others.
  3. 3. Is it possible to encourage exploration and acceptance of user profiles recommended on the basis of political diversity? If so, which factors influence this behavior?
  4. 4. Previous Approaches Previously, many paths have been pursued to make people explore diverse information / connect with others in non homophilic ways: - Filtering Algorithms [Munson et al., 2009] - Clustering, Sorting & Highlighting [Park et al., 2009; Munson and Resnick, 2010] - Visualizations [Faridani et al., 2010; Munson et al., 2013; Liao and Fu, 2014] - User Control of Political Attributes [An et al., 2014]
  5. 5. But... users do not value diversity. Previous approaches have been direct: “user is biased, here is a system to be unbiased” Users exhibit biased behavior because it is cognitively and socially easier!
  6. 6. How to recommend people with diverse views? Using homophily itself to improve first impressions with intermediary topics to focus on latent shared interests visualized in a self-image context: data portraits
  7. 7. Intermediary Topics using LDA Intermediary topics are LDA topics estimated on a user corpus with weighted information centrality above the median on a topic graph. These topics have a significantly greater political diversity of users than non intermediary topics. More info: “Finding Intermediary Topics Between People of Opposing Views: A Case Study” (SPS Workshop at SIGIR).
  8. 8. In contrast with “physical world” portraits, here the user generated content is abstracted to build a portrait. http://personas.media.mit.edu/ Data Portraits A data portrait is a visual representation of an user’s interaction data.
  9. 9. Our Platform Present a data portrait of Twitter users, and use it as a context to include recommendations of people that are likely to have diverse political views due to intermediary topics. Recommendations are generated according to a F-score between: - A notion of similarity based on Kullback-Leibler Distance between topical distributions of users. - Jaccard Similarity of user and candidate’s Intermediary Topics.
  10. 10. Design focused on self- image projection.
  11. 11. Recommendations are displayed using Circle Packing and generated using Intermediary Topics. They are clustered according to their common latent topics. Size of user avatars is proportional to similarity with the target user.
  12. 12. Implementation and Evaluation We want to measure whether users engaged with recommendations or not: - How much do they explore the recommendations? (clicks) - How much time do they spend on the site? (seconds) - Do they accept at least one recommendation?
  13. 13. “In the Wild” The platform in http://auroratwittera.cl is open for registration in an uncontrolled setting. When users signed-up in our website, we randomly assigned conditions. We got users by using Promoted Tweets and our social bot @todocl. The platform is always crawling users who discuss about politics and current events in Chile. We used those users as recommendation candidates.
  14. 14. Algorithmic conditions - Baseline: only recommend people ranked by their distance using Kullback- Leibler Distance over topic distributions. - IT: F-Score of KLD and Jaccard Similarity of Intermediary Topics. User Interface conditions - Baseline: Text-based representation of recommendations. - CP: visualization using Circle Packing. Individual Differences - Consider whether users had political content on their data portraits (True if the top-50 n-grams of the word cloud contained political terms). - Consider social and informational behavior (see the paper for details).
  15. 15. Experimental Setup Between-Subjects Negative Binomial and Logistic Regressions The model includes interaction terms N = 129 Rec. Base = 59, IT = 70 Text Base = 59, Circle Pack = 70 Political Content present in 69 users 1707 interaction events Main effects: - IT recommendations decrease exploration and likelihood of recommendation acceptance. - Circle Pack increases exploration, but not acceptance (nor decreases it - no effect). - Having political content increases likelihood of acceptance. In terms of dwell time: - There is an statistical interaction between IT, Circle Pack and Political Content! In this scenario, dwell time is increased (ES = 8.91 seconds).
  16. 16. Interpretation of Results The joint interaction of visualization (circle pack) and intermediary topics allows politically-involved users to reflect whether they accept recommendations or not. We propose that this combination of results means that those users performed conscious choices. Then, is it possible to encourage exploration of politically diverse profiles? Sometimes, but not exactly exploration. Instead, decision-making can be made less prone to cognitive biases. Which factors influence? The mixture of conditions plus context-dependant factors (political content).
  17. 17. Conclusions Indirect approaches have potential. Users might not want to change (improve) their behavior. This should be used as input in the system design. Individual differences are important in this aspect. Aim at conscious choices, not behavioral change. We might want to try to encourage conscious decision making instead of unbiased behavior. Algorithms are not enough. Visualizations neither. We need to mix both! Future Work. A qualitative study is needed to understand the why behind these results.
  18. 18. Thank you! Do you have any questions? Contact Us: eduardo.graells@telefonica.com / @carnby Reproduce Our Experiments! The source code of the project (Twitter crawler, django application, recommendation algorithms and visual interface) is available at: https://github.com/carnby/aurora Acknowledgements Daniele Quercia, Shiri Dori-Hacohen, Andrés Lucero, IUI Reviewers.
  19. 19. When users connected their accounts, the system notified them: 1) when the portrait was ready for the first time. 2) when it was updated (every 1 week).

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