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A Topological Approach for Detecting Twitter Communities with Common Interests
"... Abstract. The efficient identification of communities with common in-terests is a key consideration in applying targeted advertising and viral marketing to online social networking sites. Existing methods involve large scale community detection on the entire social network before de-termining the in ..."
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Abstract. The efficient identification of communities with common in-terests is a key consideration in applying targeted advertising and viral marketing to online social networking sites. Existing methods involve large scale community detection on the entire social network before de-termining the interests of individuals within these communities. This approach is both computationally intensive and may result in commu-nities without a common interest. We propose an efficient topological-based approach for detecting communities that share common interests on Twitter. Our approach involves first identifying celebrities that are representative of an interest category before detecting communities based on linkages among followers of these celebrities. We also study the net-work characteristics and tweeting behaviour of these communities, and the effects of deepening or specialization of interest on their community structures. In particular, our evaluation on Twitter shows that these de-tected communities comprise members who are well-connected, cohesive and tweet about their common interest.
A.: Interest classification of Twitter users using Wikipedia
- In: WikiSym+OpenSym ’13: Proceedings of the 9th International Symposium on Wikis and Open Collaboration. (Aug 2013
"... We present a framework for (automatically) classifying the relative interests of Twitter users using information from Wikipedia. Our proposed framework first uses Wikipedia to automatically classify a user’s celebrity followings into various interest categories, followed by determining the relative ..."
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We present a framework for (automatically) classifying the relative interests of Twitter users using information from Wikipedia. Our proposed framework first uses Wikipedia to automatically classify a user’s celebrity followings into various interest categories, followed by determining the relative interests of the user with a weighting compared to his/her other interests. Our preliminary evaluation on Twitter shows that this framework is able to correctly classify users’ interests and that these users frequently converse about topics that reflect both their (detected) interest and a related real-life event.
Followers Are Not Enough: Beyond Structural Communities in Online Social Networks
, 2014
"... Community detection in online social networks is typically based on the analysis of the explicit connections between users, such as “friends ” on Facebook and “followers ” on Twitter. But online users often have hundreds or even thousands of such connections, and many of these connections do not cor ..."
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Community detection in online social networks is typically based on the analysis of the explicit connections between users, such as “friends ” on Facebook and “followers ” on Twitter. But online users often have hundreds or even thousands of such connections, and many of these connections do not correspond to real friendships or more generally to accounts that users interact with. We claim that commu-nity detection in online social networks should be question-oriented and rely on additional information beyond the simple structure of the network. The concept of ‘community ’ is very general, and different questions such as “who do we interact with? ” and “with whom do we share similar interests? ” can lead to the discovery of different social groups. In this paper we focus on three types of communities beyond structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weight-ings. We show that the communities obtained in the three weighted cases are highly different from each other, and from the communities obtained by considering only the unweighted structural network. Our results confirm that asking a precise question is an unavoidable first step in community detection in online social networks, and that dif-ferent questions can lead to different insights into the network under study. 1 ar
Detecting Location-centric Communities using Social-Spatial Links with Temporal Constraints
"... Abstract. Community detection on social networks typically aims to cluster users into different communities based on their social links. The increasing popularity of Location-based Social Networks offers the op-portunity to augment these social links with spatial information, for de-tecting location ..."
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Abstract. Community detection on social networks typically aims to cluster users into different communities based on their social links. The increasing popularity of Location-based Social Networks offers the op-portunity to augment these social links with spatial information, for de-tecting location-centric communities that frequently visit similar places. Such location-centric communities are important to companies for their location-based and mobile advertising efforts. We propose an approach to detect location-centric communities by augmenting social links with both spatial and temporal information, and demonstrate its effectiveness using two Foursquare datasets. In addition, we study the effects of social, spatial and temporal information on communities and observe the fol-lowing: (i) augmenting social links with spatial and temporal information results in location-centric communities with high levels of check-in and locality similarity; (ii) using spatial and temporal information without social links however leads to communities that are less location-centric.
RESEARCH ARTICLE Followers Are Not Enough: A Multifaceted Approach to Community Detection in Online Social Networks
"... ☯ These authors contributed equally to this work. ..."
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