Vol 7, No 7 (2016) > Electrical, Electronics and Computer Engineering >

A Social Network Newsworthiness Filter Based on Topic Analysis

Chaluemwut Noyunsan, Tatpong Katanyukul, Yuqing Wu, Kanda Runapongsa Saikaew

 

Abstract:

Assessing trustworthiness of social media posts is increasingly
important, as the number of online users and activities grows. Current
deploying assessment systems measure post trustworthiness as credibility.
However, they measure the credibility of all posts, indiscriminately. The
credibility concept was intended for news types of posts. Labeling other types
of posts with credibility scores may confuse the users. Previous notable works
envisioned filtering out non-newsworthy posts before credibility assessment as
a key factor towards a more efficient credibility system. Thus, we propose to
implement a topic-based supervised learning approach that uses Term
Frequency-Interim Document Frequency (TF-IDF) and cosine similarity for
filtering out the posts that do not need credibility assessment. Our
experimental results show that about 70% of the proposed filtering suggestions
are agreed by the users. Such results support the notion of newsworthiness,
introduced in the pioneering work of credibility assessment. The topic-based
supervised learning approach is shown to provide a viable social network
filter.

Keywords: Credibility measurement; Social media analysis; Topic analysis

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