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CHI Highlights: Diversity, Discussion, and Information Consumption

For my second post on highlights from CHI this year, I’m focusing on papers related to opinion diversity and discourse quality.

  • Normative influences on thoughtful online participation
    Abhay Sukumaran, Stephanie Vezich, Melanie McHugh, Clifford Nass

    Two lab experiments on whether it is possible to foster more thoughtful commenting and participation by participants in online discussion forums by priming thoughtful norms. The first tested the effects of the behavior of other participants in the forum. The dependent variables were comment length, time taken to write the comments, and number of issue-relevant thoughts. Not surprisingly, being exposed to other thoughtful comments led people to make more thoughtful comments themselves. One of the audience members asked the question about whether this would break down with just one negative or less thoughtful comment (such as how merely one piece of litter seems to break down antilittering norms).

    The second study tested effects of visual, textual, and interaction design features on the same dependent variables. The manipulations included a more subdued vs. more playful visual design, differing CAPTCHAs (words positively correlated with thoughtfulness in the thoughtful condition and words negatively correlated with thoughtfulness in the unthoughtful condition), and different labels for the comment box. The design intended to provoke thoughtfulness did correspond to more thoughtful comments, suggesting that it is possible, at least in the lab, to design sites to prompt more thoughtful comments. For this second study in particular, I’m curious if these measures only work in the short term or if they would work in the long term and about the effects of each of the specific design features.

  • I will do it, but I don’t like it: User Reactions to Preference-Inconsistent Recommendations
    Christina Schwind, Jürgen Buder, Friedrich W. Hesse

    This paper actually appeared a in health session, but I found that it spoke much more to the issues my colleagues and I are confronting in the BALANCE project. The authors begin with the observation that most recommender systems are intended to produce content that their users will like, but that this can be problematic. In the health and wellness domain, people sometimes need to hear information that might disagree with their perspective or currently held beliefs, and so it can be valuable to recommend disagreeable information. In this Mechanical Turk-based study, subjects were equally likely to follow preference-consistent and preference-inconsistent recommendations. Following preference-inconsistent recommendations did reduce confirmation bias, but people were happier to see preference-consistent recommendations. This raises the important question: subjects may have followed the recommendation the first time, but now that they know this system gives recommendations they might not like, will they follow the recommendations less often in the future, or switch to another system altogether?

  • ConsiderIt: improving structured public deliberation
    Travis Kriplean, Jonathan T. Morgan, Deen Freelon, Alan Borning, Lance Bennett

    I really like the work Travis is doing with Reflect and ConsiderIt (which powers the Living Voters Guide) to promote more thoughtful listening and discussion online, so I was happy to see this WiP and am looking forward to seeing more!

  • Computing political preference among twitter followers
    Jennifer Golbeck, Derek L. Hansen

    This work uses follow data for Congresspeople and others on Twitter to assess the “political preference” (see comment below) of Twitter users and the media sources they follow. This approach and the information it yields has some similarities to Daniel Zhou’s upcoming ICWSM paper and, to a lesser extent, Souneil Park’s paper from CSCW this year.

    One critique: despite ample selective exposure research, I’m not quite comfortable with this paper’s assumption that political preference maps so neatly to political information preference, partly because I think this may be an interesting research question: do people who lean slightly one way or the other prefer information sources that may be more biased than they are? (or something along those lines)

In addition to these papers, Ethan Zuckerman’s closing plenary, Desperately Seeking Serendipity, touched on the topics of serendipity and homophily extensively. Zuckerman starts by suggesting the reason that people like to move to cities – even at times when cities were really quite horrible places – is, yes, for more opportunities and choices, but also “to encounter the people you couldn’t encounter in your rural, disconnected lifestyle… to become a cosmopolitan, a citizen of the world.” He goes on, “if you wanted to encounter a set of ideas that were radically different than your own, your best bet in an era before telecommunications was to move to a city.” There reasons to question this idea of cities as a “serendipity engine,” though: even people in urban environments have extremely predictable routines and just don’t go all that many places. Encounters with diverse others may not as common as idealized.

He then shifts gears to discuss what people encounter online. He walks through the argument that the idea of a Freshman Fishwrap or Daily Me is possibly quite harmful as it allows people to filter to the news that they want. Adding in social filters or getting news through our friends can make this worse. While Sunstein is concerned about this leading to polarization within the US, Zuckerman is more concerned that it leads people to see only news about where they are and less news about other places or from outside perspectives. This trend might lead people to miss important stories.

I tend to agree with the argument that surfacing coincidences or manufacturing serendipity is an incredibly powerful capability of current technology. Many of the approaches that the design community has taken to achieve this are probably not the kind of serendipity Zuckerman is looking for. I love Dopplr’s notifications that friends are also in town, but the time I spend with them or being shown around by them is time that I’m less likely to have a chance encounter with someone local or a traveler from elsewhere. The ability to filter reviews by friends may make for more accurate recommendations, but I’m also less likely to end up somewhere a bit different. Even serendipity has been repurposed to support homophily

Now, it might be that the definition of serendipity that some of the design community hasn’t quite been right. As Zuckerman notes, serendipity usually means “happy accident” now – it’s become a synonym for coincidence – and that the sagacity part of the definition has been lost. Zuckerman returns to the city metaphor, arguing for a pedestrian-level view. Rather than building tools for only efficiency and convenience, build tools and spaces that maximize the chances to interact and mix. Don’t make filters hidden. Make favorites of other communities visible, not just the user’s friends. Zuckerman elegantly compares this last feature to the traces in a city: one does not see traces left just by one’s friends, no, but traces left by other users of the space, and this gives people a chance to wander from the path they were already on. One might also overlay a game on a city, to encourage people to explore more deeply or venture to new areas.

While I like these ideas, I’m a little concerned that they will lead to somewhat superficial exposure to the other. People see different others on YouTube, Flickr, or in the news, and yes, some stop and reflect, others leave comments that make fun of them, and many others just move on to the next one. A location-based game might get people to go to new places, but are they thinking about what it is like to be there, or are they thinking about the points they are earning? This superficiality is something I worry about in my own work to expose people to more diverse political news – they may see it, but are they really considering the perspective or gawking at the other side’s insanity? Serendipity may be necessary, but I question whether it is sufficient. We also need empathy: technology that can help people listen and see others’ perspectives and situations. Maybe empathy is part of the lost idea of sagacity that Zuckerman discusses — a sort of emotional sagacity — but whatever it is, I need to better know how to design for it.

For SI and UM students who really engage with this discussion and the interweaving of cities, technology, and flows, I strongly, strongly recommend ARCH 531 (Networked Cities).

Sidelines at ICWSM

Last week I presented our first Sidelines paper (with Daniel Zhou and Paul Resnick) at ICWSM in San Jose. Slides (hosted on slideshare) are embedded below, or you can watch a video of most of the talk on VideoLectures.

Opinion and topic diversity in the output sets can provide individual and societal benefits. If news aggregators relying on votes and links to select and subsets of the large quantity of news and opinion items generated each day simply select the most popular items may not yield as much diversity as is present in the overall pool of votes and links.

To help measure how well any given approach does at achieving these goals, we developed three diversity metrics that address different dimensions of diversity: inclusion/exclusion, nonalienation, and proportional representation (based on KL divergence).

To increase diversity in result sets chosen based on user votes (or things like votes), we developed the sidelines algorithm. This algorithm temporarily suppresses a voter’s preferences after a preferred item has been selected. In comparison to collections of the most popular items, from user votes on and links from a panel of political blogs, the Sidelines algorithm increased inclusion while decreasing alienation. For the blog links, a set with known political preferences, we also found that Sidelines improved proportional representation.

Our approach differs and is complementary to work that selects for diversity or identifies bias based on classifying content (e.g. Park et al, NewsCube; ) or by classifying referring blogs or voters (e.g. Gamon et al, BLEWS). While Sidelines requires votes (or something like votes), it doesn’t require any information about content, voters, or long term voting histories. This is particularly useful for emerging topics and opinion groups, as well as for non-textual items.