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21
A crowd-powered socially embedded search engine
- Proc. ICWSM
, 2013
"... People have always asked questions of their friends, but now, with social media, they can broadcast their questions to their entire social network. In this paper we study the re-plies received via Twitter question asking, and use what we learn to create a system that augments naturally occurring “fr ..."
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Cited by 8 (3 self)
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People have always asked questions of their friends, but now, with social media, they can broadcast their questions to their entire social network. In this paper we study the re-plies received via Twitter question asking, and use what we learn to create a system that augments naturally occurring “friendsourced ” answers with crowdsourced answers. By analyzing of thousands of public Twitter questions and an-swers, we build a picture of which questions receive an-swers and the content of their answers. Because many ques-tions seek subjective responses but go unanswered, we use crowdsourcing to augment the Twitter question asking ex-perience. We deploy a system that uses the crowd to identi-fy question tweets, create candidate replies, and vote on the best reply from among different crowd- and friend-generated answers. We find that crowdsourced answers are similar in nature and quality to friendsourced answers, and that almost a third of all question askers provided unsolicit-ed positive feedback upon receiving answers from this novel information agent.
On Sampling the Wisdom of Crowds: Random vs. Expert Sampling of the Twitter Stream
"... Several applications today rely upon content streams crowdsourced from online social networks. Since real-time processing of large amounts of data generated on these sites is difficult, analytics companies and researchers are increasingly resorting to sampling. In this paper, we investigate the cruc ..."
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Cited by 6 (3 self)
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Several applications today rely upon content streams crowdsourced from online social networks. Since real-time processing of large amounts of data generated on these sites is difficult, analytics companies and researchers are increasingly resorting to sampling. In this paper, we investigate the crucial question of how to sample the data generated by users in social networks. The traditional method is to randomly sample all the data. We analyze a different sampling methodology, where content is gathered only from a relatively small subset (< 1%) of the user population namely, the expert users. Over the duration of a month, we gathered tweets from over 500,000 Twitter users who are identified as experts on a diverse set of topics, and compared the resulting expert-sampled tweets with the 1 % randomly sampled tweets provided publicly by Twitter. We compared the sampled datasets along several dimensions, including the diversity, timeliness, and trustworthiness of the information contained within them, and find important differences between the datasets. Our observations have major implications for applications such as topical search, trustworthy content recommendations, and breaking news detection.
Transient news crowds in social media
- In ICWSM
, 2013
"... Users increasingly inform themselves of the latest news through online news services. This is further accentuated by the increasingly seamless integration of social network platforms such as Twitter and Facebook into news websites, allowing easy content sharing and distribution. This makes online so ..."
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Cited by 6 (2 self)
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Users increasingly inform themselves of the latest news through online news services. This is further accentuated by the increasingly seamless integration of social network platforms such as Twitter and Facebook into news websites, allowing easy content sharing and distribution. This makes online social network platforms of particular interest to news providers. For instance, online news producers use Twitter to disseminate articles published on their website, to assess the popularity of their contents, but also as an information source to be used on itself. In this paper, we focus on Twitter as a medium to help journalists and news editors rapidly detect follow-up stories to the articles they publish. We propose to do so by leveraging “transient news crowds”, which are loosely-coupled groups that appear in Twitter around a particular news item, and where transient here reflects the fleeting nature of news. We define transient news crowds on Twitter, study their characteristics, and investigate how their characteristics can be used to discover related news. We validate our approach using Twitter data around news stories published by the BBC and Al Jazeera. 1
Finding News Curators in Twitter
"... Users interact with online news in many ways, one of them being sharing content through online social networking sites such as Twitter. There is a small but important group of users that devote a substantial amount of effort and care to this activity. These users monitor a large variety of sources o ..."
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Cited by 5 (3 self)
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Users interact with online news in many ways, one of them being sharing content through online social networking sites such as Twitter. There is a small but important group of users that devote a substantial amount of effort and care to this activity. These users monitor a large variety of sources on a topic or around a story, carefully select interesting material on this topic, and disseminate it to an interested audience ranging from thousands to millions. These users are news curators, and are the main subject of study of this paper. We adopt the perspective of a journalist or news editor who wants to discover news curators among the audience engaged with a news site. We look at the users who shared a news story on Twitter and attempt to identify news curators who may provide more information related to that story. In this paper we describe how to find this specific class of curators, which we refer to as news story curators. Hence, we proceed to compute a set of features for each user, and demonstrate that they can be used to automatically find relevant curators among the audience of two large news organizations.
Deep Twitter Diving: Exploring Topical Groups in Microblogs at Scale
- In Proc. of CSCW
, 2014
"... We present a semanticmethodology to identify topical groups in Twitter on a large number of topics, each consisting of users who are experts on or interested in a specific topic. Early studies investigating the nature of Twitter suggest that it is a social media platform consisting of a relatively s ..."
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Cited by 4 (4 self)
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We present a semanticmethodology to identify topical groups in Twitter on a large number of topics, each consisting of users who are experts on or interested in a specific topic. Early studies investigating the nature of Twitter suggest that it is a social media platform consisting of a relatively small section of elite users, producing information on a few pop-ular topics such as media, politics, and music, and the gen-eral population consuming it. We show that this characteri-zation ignores a rich set of highly specialized topics, ranging from geology, neurology, to astrophysics and karate – each being discussed by their own topical groups. We present a de-tailed characterization of these topical groups based on their network structures and tweeting behaviors. Analyzing these groups on the backdrop of the common identity and bond the-ory in social sciences shows that these groups exhibit charac-teristics of topical-identity based groups, rather than social-bond based ones. Author Keywords Topical groups; identity-based groups; Twitter; topical ex-perts; seekers of topical information;
Inferring User Interests in the Twitter Social Network
"... We propose a novel mechanism to infer topics of interest of individual users in the Twitter social network. We observe that in Twitter, a user generally follows experts on various topics of her interest in order to acquire information on those topics. We use a methodology based on social annotations ..."
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Cited by 3 (1 self)
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We propose a novel mechanism to infer topics of interest of individual users in the Twitter social network. We observe that in Twitter, a user generally follows experts on various topics of her interest in order to acquire information on those topics. We use a methodology based on social annotations (proposed earlier by us) to first deduce the topical exper-tise of popular Twitter users, and then transitively infer the interests of the users who follow them. This methodology is a sharp departure from the traditional techniques of in-ferring interests of a user from the tweets that she posts or receives. We show that the topics of interest inferred by the proposed methodology are far superior than the topics extracted by state-of-the-art techniques such as using topic models (Labeled LDA) on tweets. Based upon the proposed methodology, we build a system Who Likes What, which can infer the interests of millions of Twitter users. To our knowledge, this is the first system that can infer interests for Twitter users at such scale. Hence, this system would be particularly beneficial in developing personalized recom-mender services over the Twitter platform.
Who is the Barbecue King of Texas?: ⇤ A Geo-Spatial Approach to Finding Local Experts on Twitter
"... This paper addresses the problem of identifying local experts in so-cial media systems like Twitter. Local experts – in contrast to gen-eral topic experts – have specialized knowledge focused around a particular location, and are important for many applications includ-ing answering local information ..."
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Cited by 3 (1 self)
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This paper addresses the problem of identifying local experts in so-cial media systems like Twitter. Local experts – in contrast to gen-eral topic experts – have specialized knowledge focused around a particular location, and are important for many applications includ-ing answering local information needs and interacting with com-munity experts. And yet identifying these experts is difficult. Hence in this paper, we propose a geo-spatial-driven approach for identify-ing local experts that leverages the fine-grained GPS coordinates of millions of Twitter users. We propose a local expertise framework that integrates both users ’ topical expertise and their local authority. Concretely, we estimate a user’s local authority via a novel spatial proximity expertise approach that leverages over 15 million geo-tagged Twitter lists. We estimate a user’s topical expertise based on expertise propagation over 600 million geo-tagged social con-nections on Twitter. We evaluate the proposed approach across 56 queries coupled with over 11,000 individual judgments from Ama-zon Mechanical Turk. We find significant improvement over both general (non-local) expert approaches and comparable local expert finding approaches.
Examining Lists on Twitter to Uncover Relationships between Following, Membership and Subscription
"... We report on an exploratory analysis of pairwise relationships between three different forms of information consumption on Twitter viz., following, listing and subscribing. We develop a systematic framework to examine the relationships between these three forms. Using our framework, we conducted an ..."
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Cited by 2 (1 self)
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We report on an exploratory analysis of pairwise relationships between three different forms of information consumption on Twitter viz., following, listing and subscribing. We develop a systematic framework to examine the relationships between these three forms. Using our framework, we conducted an empirical analysis of a dataset from Twitter. Our results show that people not only consume information by explicitly following others, but also by listing and subscribing to lists and that the people they list or subscribe to are not the same as the ones they follow. Our work has implications for understanding information propagation and diffusion via Twitter and for generating recommendations for adding users to lists, subscribing and merging or splitting them.
Recognizing Skill Networks and Their Specific Communication and Connection Practices
"... Social networks are a popular medium for building and maintaining a professional network. Many studies exist on general communication and connection practices within these networks. However, studies on expertise search sug-gest the existence of subgroups centered around a particular profession. In t ..."
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Cited by 1 (1 self)
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Social networks are a popular medium for building and maintaining a professional network. Many studies exist on general communication and connection practices within these networks. However, studies on expertise search sug-gest the existence of subgroups centered around a particular profession. In this paper, we analyze commonalities and differences between these groups, based on a set of 94,155 public user profiles. The results confirm that such subgroups can be recognized. Further, the average number of connec-tions differs between groups, as a result of differences in in-tention for using social media. Similarly, within the groups, specific topics and resources are discussed and shared, and there are interesting differences in the tone and wording the group members use. These insights are relevant for inter-preting results from social media analyses and can be used for identifying group-specific resources and communication practices that new members may want to know about.
Characterizing Information Diets of Social Media Users
"... With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organi-zations, which broadcast the same carefully-edited in-formation to all consume ..."
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Cited by 1 (1 self)
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With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organi-zations, which broadcast the same carefully-edited in-formation to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the infor-mation she consumes. Moreover, the personalized rec-ommendations that most social media sites provide also contribute towards the information consumed by indi-vidual users. In this work, we define a concept of infor-mation diet – which is the topical distribution of a given set of information items (e.g., tweets) – to character-ize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users ’ consumption diets are primarily focused towards one or two topics of their in-terest, and (iii) the personalized recommendations pro-vided by Twitter help to mitigate some of the topical imbalances in the users ’ consumption diets, by adding information on diverse topics apart from the users ’ pri-mary topics of interest.