Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST

Misinformation Analytics: Potentials of Recurrent Neural Networks and LSTM in Misinformation Detection and Handling

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Dr. Mahdi Bohlouli

Detection and handling of misinformation is one of nowadays' hot topics, which is being specially discussed and researched with the existence of social networks and exponential spreading of political fake news in the web. The main point here is to first understand (1) what is misinformation, (2) which structure does it contain (present), (3) how can it be discovered (misinformation prediction) and (4) how can it's spreading be prevented (misinformation prevention)?! In general, I would define whole this as a general term of "Misinformation Analytics (MA)". The first step in the MA is to understood what and how machine learning algorithms could support autonomous fake news detection and handling. Unsupervised learning is one of candidate solutions in this regard. Besides brainstorming on those stated questions, I will provide a short overview of an already in-progress literature review in this area and will discuss about potentials that Long Short-term Memory (LSTM), Bi-LSTM and RNN may provide to support fact checking. Finally, a brief review of the potential datasets and their associated scenarios for practical work will be reviewed.

18.05.17 - 10:15
B 016