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Sunlight Labs’ Inbox Influence: Sunlight or Sunburn?

Last week, Sunlight Labs released Inbox Influence, a set of browser extensions (Chrome, Firefox) and bookmarklets that annotate senders and entities in the body of emails with who has contributed to them and to whom they have contributed.

I really like the idea of using browser plugins to annotate information people encounter in their regular online interactions. This is something we’re doing on a variety of projects here, including AffectCheck, BALANCE, and Rumors. I think that tools that combine personal data, in-situ, with more depth can teach people more about with whom and with what they are interacting, and this just in time presentation of information is an excellent opportunity to persuade and possibly to prompt reflection. Technically, it’s also a pretty nice implementation.

There are some reasons why this tool may not be so great, however. With Daniel Avrahami, Sunny Consolvo, James Fogarty, Batya Friedman, and Ian Smith, I recently published a paper about people’s attitudes toward the online availability of US public records, including campaign contribution records such as the ones on which Inbox Influence draws. Many respondents to our survey (mailed, no compensation, likely biased toward people who care more about this issue) expressed discomfort with these records being so easily accessible, and less than half (as of 2008) even knew that campaign contribution records were available online before they received the survey. Nearly half said that they wanted some sort of change, and a third said that this availability would alter their future behavior, i.e., they’d contribute less (take this with a grain of salt, since it is about hypothetical future behavior).

Unless awareness and attitudes have changed quite a bit from 2008, tools such as Inbox Influence create privacy violations. The data is being used and presented in ways that people did not anticipate at the time when they made the decision to donate, and at least some people are “horrified” or at least uncomfortable with this information being so easily accessible. Perhaps we just need to do better at educating potential donors about in what ways campaign contribution data may be used (and anticipate future mashups), though it is also possible that tools like this do not need to be made, or could benefit from being a bit more nuanced in when and about whom they load information.

Speaking personally, I’m not sure how I feel. On the one hand, I think that campaign contributions and other other actions should be open to scrutiny and should have consequences. If you take the money you earn from your business and donate it to support Prop 8, I want the opportunity to boycott your business. If you support a politician who wants to eviscerate the NSF, I might want to engage you in conversation about that. On the other hand, I don’t like the idea that my campaign contribution history (anything above the reporting limit) might be loaded automatically when I email a professional colleague or a student. That’s just not relevant—or even appropriate—to the context. And there are some friendships among political diverse individuals that may survive, in part, because those differences are not always made salient. So it also seems like Inbox Influence or tools that let you load, with a click, your Facebook friends’ contribution history, could sometimes cause harm.

updated viz of political blogs’ link similarity

I’ve been meaning to post a simple update to my previous visualization of political blogs’ link similarities. In the previous post, I used GEM for layout, which was not, in hindsight, the best choice.

In the visualization in this post, the edges between blogs (the nodes, colorized as liberal, independent, and conservative) are weighted as the Jaccard similarity between any two blogs. The visualization is then laid out in GUESS using multidimensional scaling (MDS) based on the Jaccard similarities.

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.

visualizing political blogs’ linking

There are a number of visualizations of political bloggers’ linking behavior, notably Adamic and Glance’s 2005 work that found political bloggers of one bias tend to link to others of the same bias. Also check out Linkfluence’s Presidential Watch 08 map, which indicates similar behavior.

These visualizations are based on graphs of when one blog links to another. I was curious to what extent this two-community behavior occurs if you include all of the links from these blogs (such as links to news items, etc). Since I have link data for about 500 blogs from the news aggregator work, it was straightforward to visualize a projection of the bipartite blog->item graph. To classify each blog as liberal, conservative, or independent, I used a combination of the coding from Presidential Watch, Wonkosphere, and my own reading.

Projection of links from political blogs to items (Oct - Nov 2008)

Projection of links from political blogs to items (Oct - Nov 2008). Layout using GEM algorithm in GUESS.

The visualization shows blogs as nodes. Edges represent shared links (at least 6 items must be shared before drawing an edge) and are sized based on their weight. Blue edges run between liberal blogs, red edges between conservative blogs, maroon between conservative and independent, violet blue between liberal and independent, purple between independent blogs, and orange between liberal and conservative blogs. Nodes are sized as a log of their total degree. This visualization is formatted to appear similar to the Adamic and Glance graph, though there are some important differences, principally because this graph is undirected and because I have included independent blogs in the sample.

This is just a quick look, but we can see that the overall linking behavior still produces two fairly distinct communities, though a bit more connected than just the graph of blog to blog links. It’d be fun to remove the linked blog posts from this data (leaving mostly linked news items) to see if that changes the picture much. Are some media sources setting the agenda for bloggers of both parties, or are the conservative bloggers reading and reacting to one set news items and liberal bloggers reading and reacting to another? I.e., is the homophily primarily in links to opinion articles, or does it also extend to the linked news items?

I’m out of time at this point in the semester, though, so that will have to wait.

bias mining in political bloggers’ link patterns

I was pretty excited by the work that Andy Baio and Joshua Schachter did to identify and show the political leanings in the link behavior of blogs that are monitored by Memeorandum. They used singular value decomposition [1] on an adjacency matrix between sources and items based on link data from 360 snapshots of Memeorandum’s front page.

For the political news aggregator project, we’ve been gathering link data from about 500 blogs. Our list of sources is less than half of theirs (I only include blogs that make full posts available in their feeds), but we do have full link data rather than snapshots, so I was curious if we would get similar results.

The first 10 columns of two different U matrices are below. They are both based on link data from 3 October to 7 November; the first includes items that had an in-degree of at least 4 (5934 items), the second includes items with an in-degree of at least 3 (9722 items). In the first, the second column (v2) seems to correspond fairly well to the political leaning of the blog; in the second, the second column (v3) is better.

I’ll be the first to say that I haven’t had much time look at these results in any detail, and, as some of the commenters on Andy’s post noted, there are probably better approaches for identifying bias than SVD. If you’d like to play too, you can download a csv file with the sources and all links with an in-degree >= 2 (21517 items, 481 sources). Each row consists of the source title, source url, and then a list of the items the source linked to from 3 October to 7 November. Some sources were added part way though this window, and I didn’t collect link data from before they were added.

[1] One of the more helpful singular value decomposition tutorials I found was written by Kirk Baker and is available in PDF.

US political news and opinion aggregation

Working with Paul Resnick and Xiaodan Zhou, I’ve started a project to build political news aggregators that better reflect diversity and represent their users, even when there is an unknown political bias in the inputs. We’ll have more on this to say later, but for now we’re making available a Google gadget based on a prototype aggregator’s results.

The list of links is generated from link data from about 500 blogs and refreshed every 30 minutes. Some of the results will be news stories, some will be op-ed columns from major media services, others will be blog posts, and there are also some other assorted links.

At this early point in our work, the results tend to be more politically diverse than an aggregator such as Digg, but suffer from problems with redundancy (we aren’t clustering links about the same story yet). As our results get better, the set of links the gadget shows should improve.

Update 15 December: I twittered last week that I’ve added bias highlighting to the widget, but I should expand a bit on that here.

Inspired by Baio and Schachter’s coloring of political bias on Memeorandum, I’ve added a similar feature to the news aggregator widget. Links are colored according the average bias of the blogs linking to them. This is not always a good predictor of the item’s bias or whether it better supports a liberal or conservative view. Sometimes a conservative blogger writes a post to which more liberal bloggers than conservative bloggers, and in that case, the link will be colored blue.

If you don’t like the highlighting, you can turn it off in the settings.


A note came across the anthrodesign list earlier this week about, a website on which people who disagree about a certain issue are paired up, given some guidelines for productive conversation, and then have a facilitated discussion in reaction to a provided scenario.

One of my concerns about virtual community and online discourse has been the ease with which people can choose to associate primarily with people who agree with them. This is particularly true in the blogosphere and is why I stopped political blogging after the 2004 election. Lately I’ve been looking more towards online interactions that are not overtly about politics as a possible source for building mutual respect and understanding as a foundation for constructive civic engagement. I suppose that this is a more subtle approach.

RedBlue, in contrast, is anything but subtle. I’m very interested in seeing where this goes. They’re currently recruiting testers, and if anyone happens to give it a try, I’d love to hear your thoughts.

California sues automakers

While reading the news the other day, I noticed that California’s attorney general, Bill Lockyer, filed suit against six automakers. Interesting. Lockyer is “demanding that they pay for environmental damage caused by the emissions of their vehicles.”

Interesting, but irrational (as far as I can tell).

To the best of my knowledge, the automakers have remained in compliance with state and federal laws regulating emissions. Turning and filing suit against companies that have been compliant feels a bit like playing “gotcha.” If the emissions laws were insufficient, he should be complaining about the federal government and the state of California. It he’s looking for the real culprit, though, he would be suing the people of California who just keep on using their cars so much despite the known environmental consequences. That probably wouldn’t work so well for Mr. Lockyer, though, as he currently wants those same constituents to elect him to state treasurer in the fall.