Since the introduction of the Internet, China's networked public sphere has become a critical site in which various actors compete to shape public opinion and promote or forestall legal and political change. This paper examines how members of an online public, the Tianya Forum, conceptualized and discussed law in relation to a specific event, the 2008 Sanlu milk scandal. Whereas previous studies suggest the Chinese state effectively controls citizens' legal consciousness via propaganda, this analysis shows that the construction of legality by the Tianya public was not a top‐down process, but a complex negotiation involving multiple parties. The Chinese state had to compete with lawyers and outspoken media to frame and interpret the scandal for the Tianya public and it was not always successful in doing so. Data show further how the online public framed the food safety incident as indicative of fundamental problems rooted in China's political regime and critiqued the state's instrumental use of law.

News aggregators rely on links and users votes to select and present subsets of the large quantity of news and opinion items generated each day. Opinion diversity in the output sets can provide several benefits. We outline a range of diversity goals and discuss user reactions to a pilot implementation that selects for diversity as well as popularity. We then describe plans for research on alternative presentations and their impacts on users.

Social news aggregator services generate readers’ subjective reactions to news opinion articles. Can we use those as a resource to classify articles as liberal or conservative, even without knowing the self-identified political leaning of most users? We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, based on the assumption that liberal users will vote for liberal articles more often, and similarly for conservative users and articles. Starting from a few labeled articles and users, the algorithms propagate political leaning labels to the entire graph. In cross-validation, the best algorithm achieved 99.6% accuracy on held-out users and 96.3% accuracy on held-out articles. Adding social data such as users’ friendship or text features such as cosine similarity did not improve accuracy. The propagation algorithms, using the subjective liking data from users, also performed better than an SVM based text classifier, which achieved 92.0% accuracy on articles.

No abstract

Aggregators rely on votes, and links to select and present subsets of the large quantity of news and opinion items generated each day. Opinion and topic diversity in the output sets can provide individual and societal benefits, but simply selecting the most popular items may not yield as much diversity as is present in the overall pool of votes and links. In this paper, we define three diversity metrics that address different dimensions of diversity: inclusion, non-alienation, and proportional representation. We then present the Sidelines algorithm – which temporarily suppresses a voter’s preferences after a preferred item has been selected – as one approach to increase the diversity of result sets. In comparison to collections of the most popular items, from user votes on Digg.com 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. In an online experiment using blog link data as votes, readers were more likely to find something challenging to their views in the Sidelines result sets. These findings can help build news and opinion aggregators that present users with a broader range of topics and opinions.