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Preventing Filter Bubbles and Underprovision in Online Communities with Social Curation Algorithms
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Preventing Filter Bubbles and Underprovision in Online Communities with Social Curation Algorithms


In ongoing work, we are experimenting with data-driven approaches to support communication and socialization processes in online communities. Specifically, our work addresses the problems that often …

In ongoing work, we are experimenting with data-driven approaches to support communication and socialization processes in online communities. Specifically, our work addresses the problems that often arise due to the use of automated methods for curating participants' contributions. Simple binary constructs such as “helpful,” “like,” or “thumbs up/down” have become dominant mechanisms for organizing user-contributed content. Readers' feedback on collected content is aggregated and used to create information displays, ranking content by attributes such as what is “most helpful” or “most liked.” The result is a curated collection of participants’ contributions.

Given the tendency for people to access items in the order of presentation and to satisfice rather than satisfy their needs for information, the aforementioned curation algorithms largely determine what information participants are exposed to (i.e., creating a filter bubble). We are undertaking a systematic investigation of communities that employ such mechanisms in order to better understand how curation algorithms impact users. Existing research suggests social voting mechanisms have unintended consequences (e.g., that there is little turnover in what is “most helpful,” even when new, high-quality content is added, that some kinds of content are consistently hidden because they receive few votes), and we are studying the effects of those consequences: how do user perceptions and behavior change based on the information shown (or hidden)? How does the information shown (or hidden) influence what information users contribute? Do curation algorithms display homogeneous information that alienates underrepresented users? The many possible combinations of features of users, content, and information displays present a complex problem for automated curation.

The research problem is fundamentally socio-technical, and our project employs a multi-method approach that focuses on four characteristics of the algorithms and users: (1) contributor characteristics (e.g., gender, reputation), (2) content characteristics (e.g., writing style, key words), (3) the perceived value of curated content (e.g., “helpful” votes received), and (4) the presentation algorithm(s) (e.g., reverse helpfulness rank). We are conducting automated analyses of the content and its presentation and are planning a survey of users. Our study includes multiple communities in three different domains (health, entertainment, and news). We hope to identify the conditions under which contributions and/or contributors that exhibit certain properties are systematically ranked lower (or higher) than others and how the information displayed impacts user behavior.

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  • I’d like to start out by showing you some examples of the types of OCS that we’re studying. This is a screenshot from the Internet Movie Database (IMDb), a shared space where users can contribute ratings and reviews of films.-The Godfather is currently listed as #2 on IMDb’s top movie list, and you can see that over 1500 users have contributed reviews. -In terms of what users are doing at this OCS, of course they’re critiquing films and expressing their opinions. However, they’re also often trying to make sense of the deeper meaning of a film.-Now, I’d like to point out how reviews are organized; you can see that users are asked whether or not a review was useful. Votes are then aggregated over all users and displayed above the review; reviews are then listed in rank order by usefulness, and that’s the default filter “Best.”
  • The second example is from YahooNews, which aggregates news stories and provides a place for users to leave comments. This is obviously an article that was in the news this week, and at the time I took the screenshot, the article had been published for about 2.5 hours and there were almost 2000 user comments on it. In terms of what we can observe users doing here, of course there’s a good deal of sounding off about issues, but we also observe users trying to process an event that’s happened or make sense of it.-In terms of organization, we again see that a voting mechanism is in place, only here users can give a thumbs up or down on a comment. The default display is “Popular Now,” which prioritizes contributes that are recent and that have received positive votes.
  • We are interested in the biases in the information display that result from the use of binary voting in OCS. Our working definition is…In other words, the information display gives users a particular take on “what others think.”The prominently displayed content is what users see and read, and if they have approached the OCS in an effort to make sense of a news item or a film or an issue, their impression of “what others think” is likely to be based on the highly-ranked content.And we know that’s likely to happen because there’s a great deal of research on how we interact with information, particularly when it’s presented to us as a ranked list of items.
  • An example of a type of bias that many of us would consider undesirable.In our examination of the IMDb voting mechanism, we’ve found that generally speaking, women’s contributions are perceived as less “useful” as compared to men’s. Here, I show two reviews of the classic movie Casablanca. They illustrate the differences in writing style between genders, as well as the fact that women’s reviews receive fewer total votes and fewer “useful” votes. We’re quite convinced that when a user looks up a film at IMDb in order to see “what people think” what he or she is getting is the men’s impressions of the film.
  • Now, to contrast, here’s one that many might find desirable.At three different OCS, we studied samples of forums reviewing various products, movies and services.A consistent finding was that front-page reviews (i.e., those deemed as helpful/useful by the crowd), are better edited than are those on latter pages.In this example, you see a review of a phone at Amazon. The content of the review is actually pretty comparable to highly-ranked reviews, but you can see that the presentation is not very standard (no capitalization, non-standard abbreviations).
  • We want to better understand social voting bias and we need a way to study bias systematically.The current paper sketches our framework for a cross-system study of bias in OCS using binary voting mechanisms.
  • Along with our students, we’ve been collecting a diverse set of examples of OCS, and we’ve been characterizing them with respect to their organization genomes, based on Malone and colleague’s framework. As you can see, we’re able to identify OCS that have essentially the same organizational genomes across many domains (such as health, news and entertainment). We’re discovering the “social voting genomes” of these systems as well. Social voting genome has three genes (voting construct, default information display, alternative displays). It’s interesting to note that different voting constructs are used, and it will be interesting in future work to consider if it makes any difference which constructs users vote on.
  • Casting our net wide, what we’re doing right now is having a look at our example OCS, in order to come up with a taxonomy of biases that might occur. In other words, we’re trying to understand which properties of contributors and contribution characteristics might be susceptible to ranking bias.
  • One obvious challenge for us in our study of bias in OCS is getting a feel for who does what, and we plan to undertake a survey of users across several OCS. There are really only three activities in the OCS, consume, share and vote on content, and this table shows the possible 7 participant roles, based on combinations of these activities. Ideally, every participant would be fully engaged, but of course we know that’s very unlikely. What we plan to do is to survey participants across several OCS (with similar genomes). What we want to examine is how the distribution of participant roles relates to the biases we observe. Of course, the less fully engaged users are, the more undesirable biases we might expect.
  • Finally,
  • We’ve advocated for a better understanding of biases that result when binary social voting mechanisms are used in OCS.Once we develop ways to detect and better understand which types of biases occur in a given system, we argue that we might be able to exploit it, by revealing its presence to users. We’d like to entice them to increase their participation across all activities in an OCS, as well as to go beyond just using the default information displays. For example, in our IMDb example, if we revealed to users that the majority of top reviews written on a film of interest were written by men, if that might make them curious enough to explore the alternative displays that you see here, and perhaps become exposed to more diverse content.


  • 1. Preventing Filter Bubbles and Underprovision in Online Communities with Social Curation Algorithms: Data-driven approaches to measuring “bias” Jahna Otterbacher Open University Cyprus, Nicosia CYPRUS Libby Hemphill Illinois Institute of Technology, Chicago USA
  • 2. Social Curation Algorithms in Online Communities • Low barriers to entry • Users contribute to a collection of shared content • Users judge the value of content via binary voting • Aggregated votes used in information display(s) Aarhus University, 3 October 2013
  • 3. Aarhus University, 3 October 2013
  • 4. Aarhus University, 3 October 2013
  • 5. Bias • Content with particular properties systematically ranked higher/lower than others • Information display gives users a particular take on “what others think” • Prominently displayed content is what users see and read • Users often do not change default settings • They place trust in information displays Aarhus University, 3 October 2013
  • 6. Gender bias at IMDb Aarhus University, 3 October 2013
  • 7. Editing bias at Amazon, IMDb and Yelp Aarhus University, 3 October 2013
  • 8. Underprovision problem • When social curation is used: “too many people rely on others to contribute without doing so themselves.” [Gilbert, 2013] • Study of Reddit • Most communities suffer from some degree of free riding • At Reddit, users’ contributions being buried led to disincentives for contributions • “…it’s such an incredible resource when the comments are flowing, but if your post gets buried for whatever reason, it’s painfully anti- climactic.” Aarhus University, 3 October 2013
  • 9. Our perspective • Bias is inevitable and is not necessarily bad • Presence of bias could be revealed to users • Research questions • What types of biases may occur? • Under what circumstances? • How can we study bias across systems? Aarhus University, 3 October 2013
  • 10. Proposed framework • Find diverse examples of systems • Taxonomy of biases • Participation rates and participant roles • Examine correlations between system/participant characteristics and observed biases • Generate ideas of how to respond Aarhus University, 3 October 2013
  • 11. Aarhus University, 3 October 2013
  • 12. Bias taxonomy • Contributor characteristics • Demographics • Level, type of activities • Information disclosure • Contribution characteristics • Writing style (e.g., narrative/reporting) • Content (e.g., uniqueness/conformity) • Metadata (e.g., time posted) Aarhus University, 3 October 2013
  • 13. Participation rates & roles Aarhus University, 3 October 2013
  • 14. Correlations • How are system and participant characteristics correlated to the biases that we observe? • Are more information displays necessarily better? • Which default display leads to more/less diversity with respect to a given characteristic of content? Aarhus University, 3 October 2013
  • 15. Final thoughts • Can we exploit bias in order to • Entice users to participate in all activities? • Convince users to question default information displays? Aarhus University, 3 October 2013
  • 16. Thank you! jahna.otterbacher@gmail.com Aarhus University, 3 October 2013