Truth assessment of objective facts extracted from tweets: A case study on world cup 2014 game facts

By understanding the tremendous opportunities to work with social media data and the acknowledgment of the negative effects social media messages can have, a way of assessing truth in claims on social media would not only be interesting but also very valuable. By making use of this ability, applications using social media data could be supported, or a selection tool in research regarding the spread of false rumors or 'fake news' could be build. In this paper, we show that we can determine truth by using a statistical classifier supported by an architecture of three preprocessing phases. We base our research on a dataset of Twitter messages about the FIFA World Cup 2014. We determine the truth of a tweet by using 7 popular fact types (involving events in the matches in the tournament such as scoring a goal) and we show that we can achieve an F1-score of 0.988 for the first class; the Tweets which contain no false facts and an F1-score of 0.818 on the second class; the Tweets which contain one or more false facts.