Connecting users across social media sites: a behavioral-modeling approach

By Reza Zafarani, Huan Liu. Knowledge Discovery and Data Mining, 2013

439 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/9a3b26ea2b2519572b4ac6f3e07215a10b43dce3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Sociology and Political Science

Citations in the corpus

#mytweet via Instagram: Exploring user behaviour across multiple social networks (2015, background): “Automated user linkage aims to link user accounts among different social networks that belong to the same person in the real world [10], [11].”
Mining target users for online marketing based on App Store data (2015, neutral): “Zafarani and Liu [19] present approaches to identify individuals across social media sites, such that a better profile of a user can be built by integrating sources of complementary information from multiple social networks.”
Sarcasm Detection on Twitter: A Behavioral Modeling Approach (2015, positive): “Therefore, SCUBA incorporates a behavioral modeling approach [42] for sarcasm detection that utilizes features which capture the diﬀerent forms of sarcasm.”
Predict Anchor Links across Social Networks via an Embedding Approach (2016, background): “The second category of approaches is supervised [Kong et al. , 2013; Zafarani and Liu, 2013], with which the anchor link prediction problem is solved under the supervision of observed anchor links (e.g., some users may explicitly mark their accounts in different social networks in their homepages,…”
When is it biased?: assessing the representativeness of twitter's streaming API (2014, background): “One way to incorporate these other views is to cross-reference users from Twitter with other social media outlets, one such solution has been proposed in [20].”
When Social Influence Meets Item Inference (2015, methodology): “cross multiple OSNs. Account matching across multiple OSNs plays a crucial role to discover the user behaviors in multiple OSNs. Existing schemes achieve good performance by employing link prediction [10, 17, 18, 30], which assumes that similar nodes belong to the same user and have a high potential”
COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency (2015, methodology): “Following [39, 40, 28], we deﬁne a similarity function for usernames.”