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Towards Customizing Credibility in Different Contexts: Languages, Topics and Locations - A Twitter Case Study

Proceeding: The International Conference on Digital Information Processing, E-Business and Cloud Computing (DIPECC)

Publication Date:

Authors : ;

Page : 218-224

Keywords : Social networks; Micro-blogs; Credibility; Arabic; Machine Learning;

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Even though online social networks (ONS) have been increasingly used as a source of news and information, the credibility of that easily-available information may be questionable. In this paper, we review selected literature related to information credibility focusing more on micro-blogging credibility as a part of on-going research. From this review it can be concluded that credibility is situational and contextual; it varies from one context to another. We propose to examine messages related to confirmed news topics and false rumors topics and then study the effect of three dimensions: language, topic content, and location on the detection features utilized by the existing work. Firstly, we will check if the frequency distribution of selected features remains similar or changes according these dimensions. We then examine different available classifying techniques and study the effect of each context on the credibility classifying results. Another goal is to report on user studies that explore how people from different environments perceive and judge information credibility on Twitter. We believe the analysis of our results will improve existing credibility measurement by helping us in selecting the most suitable features for information credibility classification in each context

Last modified: 2013-06-20 21:07:38