Tweet Extraction for News Production Considering Unreality

Acquiring information on incidents and accidents from social media can be useful for broadcasters to report news faster. However, many tweets including words related to incidents and accidents are actually irrelevant to real events, for example, “Backdraft's explosion scene was impressive!!!” Social media contains many comments on events in unreal worlds such as movies, animations and dramas, and it is time-consuming to discriminate these tweets manually. This work presents a method for automatically extracting useful tweets for news reports by focusing on “unreal” information. We first prepare unreal tweets as learning data and use a distributed representation and features that can determine if a tweet is real or unreal. By adding the features of a neural network, we generate a learning model that can effectively discriminate whether a tweet includes information on actual incidents or accidents. Results of evaluations revealed that the proposed method achieved a 3.8-point higher F-measure than the baseline method.