Twitter is a microblogging and social networking service with millions of members and growing at a tremendous rate. With the buzz surrounding the service have come claims of its ability to transform the way people interact and share information and calls for public figures to start using the service. In this study, we are interested in the type of content that legislators are posting to the service, particularly by members of the United States Congress. We read and analyzed the content of over 6,000 posts from all members of Congress using the site. Our analysis shows that Congresspeople are primarily using Twitter to disperse information, particularly links to news articles about themselves and to their blog posts, and to report on their daily activities. These tend not to provide new insights into government or the legislative process or to improve transparency; rather, they are vehicles for self-promotion. However,Twitter is also facilitating direct communication between Congresspeople and citizens, though this is a less popular activity. We report on our findings and analysis and discuss other uses of Twitter for legislators. IntroductionTalk is cheap, except when Congress does it.Cullen Hightower Twitter (http://www.twitter.co) is a popular microblogging and social networking service with approximately 7 million members. Twitter is one of the fastest growing sites on the Web in terms of usage (one estimate puts year-over-year growth at over 1300%; http://blog.nielsen.com/nielsenwire/ online_mobile/twitters-tweet-smell-of-success/). The dramatic growth has created significant buzz about the site.Twitter supporters see it as a potential solution for many information sharing problems. One manifestation of this is the TweetCongress initiative (http://www.tweetcongress. org/). TweetCongress is a grass-roots Web-based campaign with the goal of promoting transparency in government by encouraging representatives in Congress to use Twitter. There is very little work that has studied how Twitter is used, particularly with respect to the content of the posts (Mischaud, 2008;Honey & Herring, 2009). In this study, we present a thorough analysis of how Congresspeople use the service. We look at the demographics (including party affiliation and home state), the frequency of posts, and, most important, the content of the messages they post. For this latter analysis, we analyzed the content of the vast majority of messages that had been posted by members of Congress to obtain a thorough picture of the type and intent of the messages. We have chosen not to study the underlying social network (followers, following, and friends), but this is a rich space for future work.Based on this analysis, we show that Congress members are largely using Twitter to communicate the same type of information their offices would share in other media. They post links to news articles, blog posts, and descriptions of upcoming activities and use the tweets like titles for mini press releases. Congresspeople also use Twitter in the stereotypical way...

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Social media is a place where users present themselves to the world, revealing personal details and insights into their lives. We are beginning to understand how some of this information can be utilized to improve the users' experiences with interfaces and with one another. In this paper, we are interested in the personality of users. Personality has been shown to be relevant to many types of interactions; it has been shown to be useful in predicting job satisfaction, professional and romantic relationship success, and even preference for different interfaces. Until now, to accurately guage users' personalities, they needed to take a personality test. This made it impractical to use personality analysis in many social media domains. In this paper, we present a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile. We will describe the type of data collected, our methods of analysis, and the machine learning techniques that allow us to successfully predict personality. We then discuss the implications this has for social media design, interface design, and broader domains.

The growth of Web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences. Our research looks particularly at assigning trust in Web-based social networks and investigates how trust information can be mined and integrated into applications. This article introduces a definition of trust suitable for use in Web-based social networks with a discussion of the properties that will influence its use in computation. We then present two algorithms for inferring trust relationships between individuals that are not directly connected in the network. Both algorithms are shown theoretically and through simulation to produce calculated trust values that are highly accurate.. We then present TrustMail, a prototype email client that uses variations on these algorithms to score email messages in the user's inbox based on the user's participation and ratings in a trust network.

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Online communities that allow their users to express their personal preferences, such as the members they trust and the products they appreciate, are becoming increasingly popular. Exploiting these communities as playgrounds for sociological research we present two frameworks for analyzing the correlation between interpersonal trust and interest similarity. We obtain empirical results from applying the two frameworks on two real, operational communities, that suggest there is a strong correlation between both trust and interest similarity. We believe our findings particularly relevant for ongoing research in recommender systems and collaborative filtering, where people are suggested products based on their similarity with other customers, and propose ways in which trust models can be integrated into these systems.

Online social networks, where users maintain lists of friends and express their preferences for items like movies, music, or books, are very popular. The Web-based nature of this information makes it ideal for use in a variety of intelligent systems that can take advantage of the users' social and personal data. For those systems to be effective, however, it is important to understand the relationship between social and personal preferences. In this work we investigate features of profile similarity and how those relate to the way users determine trust. Through a controlled study, we isolate several profile features beyond overall similarity that affect how much subjects trust hypothetical users. We then use data from FilmTrust, a real social network where users rate movies, and show that the profile features discovered in the experiment allow us to more accurately predict trust than when using only overall similarity. In this article, we present these experimental results and discuss the potential implications for using trust in user interfaces. ACM Reference Format:Golbeck, J. 2009. Trust and nuanced profile similarity in online social networks.

Social networks are growing in number and size, with hundreds of millions of user accounts among them. One added benefit of these networks is that they allow users to encode more information about their relationships than just stating who they know. In this work, we are particularly interested in trust relationships, and how they can be used in designing interfaces. In this paper, we present FilmTrust, a website that uses trust in webbased social networks to create predictive movie recommendations. Using the FilmTrust system as a foundation, we show that these recommendations are more accurate than other techniques when the user's opinions about a film are divergent from the average. We discuss this technique both as an application of social network analysis, as well as how it suggests other analyses that can be performed to help improve collaborative filtering algorithms of all types. Report Documentation Page Form Approved OMB No. 0704-0188Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.