Discovering similar Twitter accounts using semantics

Discovering similar Twitter accounts using semantics

On daily basis, millions of Twitter accounts post a vast number of tweets including numerous Twitter entities (mentions, replies, hashtags, photos, URLs). Many of these entities are used in common by many accounts. The more common entities are found in the messages of two different accounts, the more similar, in terms of content or interest, they tend to be. Towards this direction, we introduce...

Discovering Similar Users on Twitter

This work studies the problem of discovering “similar” users at Twitter, where we define two users to be similar if they produce content similar to each other. The discovery of top similar accounts for each Twitter user has a variety of applications at Twitter including user recommendations and advertiser targeting. Although the discovery of similar results is a well studied problem in informat...

Behavioral Tracing of Twitter Accounts

“Trolls” individuals who engage in malicious behavior are a common occurrence within online communities. Yet simply banning accounts associated with trolls is often ineffective as individuals may register new accounts under pseudonyms and resume their activity. In this paper, we demonstrate how this can be addressed through a behavioral trace. Specifically, we show that by analyzing the posts o...

Hierarchical Fuzzy Clustering Semantics (HFCS) in Web Document for Discovering Latent Semantics

This paper discusses about the future of the World Wide Web development, called Semantic Web. Undoubtedly, Web service is one of the most important services on the Internet, which has had the greatest impact on the generalization of the Internet in human societies. Internet penetration has been an effective factor in growth of the volume of information on the Web. The massive growth of informat...

Discovering Semantics in Multimedia Content Using Wikipedia