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

Eduardo Graells-Garrido*Telefónica R&D, Chile

Mounia LalmasYahoo, UK

Ricardo Baeza-YatesUPF, Catalonia / Univ. of Chile

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.

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?

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]

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!

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

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).

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.

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.

Design focused on self-image projection.

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.

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?

“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.

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).

Experimental Setup

Between-SubjectsNegative Binomial and Logistic Regressions The model includes interaction terms N = 129Rec. Base = 59, IT = 70Text Base = 59, Circle Pack = 70Political Content present in 69 users1707 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).

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).

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.

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.

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).