Towards Rumors Detection Framework for Social Media

A Rumor is considered as unverified pieces of information circulating, that arise in the context of uncertainty, with negative impact, and falsely attributes. Unfortunately, terribly damaging form of communication are the results of rumors. Rumors spread on social media with no exception, and only serve to amplify the negative effects on people and businesses. This paper aims to present literature related to rumor detection on social network and try to find a link on how human behavior is affected by it. Therefore, it surveys the rumors detection frameworks, algorithms, and computational techniques that help in detecting and blocking rumors from spreading on social media. Also, attributes that may identify and describe a rumor and human behavior towards rumors are gathered, unified, and arranged in an integrated recommended list. This list of attributes may be the guide for detecting and capturing rumors with their changeable inconstant form. As a result, from this trial a proposed framework is presented to offer an idea for dealing with human behavior on rumors. This model presents open issues and forwarded ideas to provide an insight for future work in the area of building Rumor-Human Behavior computational models. General Terms Pattern Recognition, Machine learning, Social media computation.

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