Filtering Structures for Microblogging Content

Abstract

In the last years, microblogging systems have encountered a large success. After 7 years of existence Twitter claims more than 271 million active accounts leading to 500 million tweets per day. Microblogging systems rely on the all-or-nothing paradigm: a user receives all the posts from an account s/he follows. A consequence for a user is the risk of flooding, i.e., the number of posts received from all the accounts s/he follows implies a time-consuming scan of her/his feed to find relevant updates that match his interests. To avoid user flooding and to significantly diminish the number of posts to be delivered by the system, the authors propose to introduce filtering on top of microblogging systems. Driven by the shape of the data, the authors designed different filtering structures and compared them analytically as well as experimentally on a Twitter dataset which consists of more than 2.1 million users, 15.7 million tweets and 148.5 million publisher-follower relationships.

Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

“…Groth et al (2010) analyzed the scientific discourse on the web using bibliometric techniques like citation and keyword similarity maps and concluded that scientific discourse on the web is contextually relevant. Dahimene and Mouza (2015) designed various filters to reduce the number of microblogs fetched by the system, while retrieving the users' relevant updates. Zhang et al (2013) identified the influential person in online social networks based on the tie strength.…”