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Identifying Framing Bias in Online News

It has been observed that different media outlets exert bias in the way they report the news, which seamlessly influences the way that readers’ knowledge is built through filtering what we read. Therefore, understanding bias in news media is fundamental for obtaining a holistic view of a news story. Traditional work has focused on biases in... (more)

Is Virtual Citizen Science A Game?

The use of game elements within virtual citizen science is increasingly common, promising to bring increased user activity, motivation, and engagement to large-scale scientific projects. However, there is an ongoing debate about whether or not gamifying systems such as these is actually an effective means by which to increase motivation and... (more)

MFPR: A Personalized Ranking Recommendation with Multiple Feedback

Recently, recommender systems have played an important role in improving web user experiences and increasing profits. Recommender systems exploit users’ behavioral history (i.e., feedback on items) to build models. The feedback usually includes explicit feedback (e.g., ratings) and implicit feedback (e.g., browsing history, click logs),... (more)

Effects of Collective Socialization on Newcomers’ Response to Feedback in Online Communities

Collective socialization involves introducing new members to an organization as a group or cohort.... (more)

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About TSC

ACM Transactions on Social Computing (TSC) seeks to publish work that covers the full spectrum of social computing including theoretical, empirical, systems, and design research contributions. READ MORE

Special Issue on Emoji Understanding and Applications in Social Media

This special seeks original research manuscripts dealing with computer and social science research efforts on understanding social, cultural, communicative, and linguistic roles of emoji and on building novel computational methods to understand, interpret, and exploit them. READ MORE

Forthcoming Articles
Modeling User Intrinsic Characteristic on Social Media for Identity Linkage

Most users on social media have intrinsic characteristics, such as interests and political views, that can be exploited to identify and track them, thus raising privacy and identity concerns in online communities. In this paper we investigate the problem of user identity linkage on two behavior datasets collected from different experiments. Specifically, we focus on user linkage based on users' interaction behaviors with respect to content topics. We propose an embedding method to model a topic as a vector in a latent space so as to interpret its deep semantics. Then a user is modeled as a vector based on his or her interactions with topics. The embedding representations of topics are learned by optimizing the joint-objective: the compatibility between topics with similar semantics, the discriminative abilities of topics to distinguish identities, and the consistency of the same user's characteristics from two datasets. The effectiveness of our method is verified on real-life datasets and the results show that it outperforms related methods. We also analyze failure cases in the application of our identity linkage method. Our analysis shows that factors such as the visibility and variance of user behaviors and user's group psychology can result in mis-linkages. We also analyze the details of the behaviors of some representative users to understand the essential reasons for their identity being mis-linked. We find that these users have high variance level in their behaviors. According to the above experimental results, we introduce a confidence score into identity linkage to provide information about the accuracy of the method results.

Looking South: Learning Urban Perception in Developing Cities

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