Identification of Good and Bad News on Twitter

Proceedings of Recent Advances in Natural Language Processing, pages 9–17, Varna, Bulgaria, Sep 2–4, 2019. https://doi.org/10.26615/978-954-452-056-4_002 9 Identification of Good and Bad News on Twitter Piush Aggarwal University of Duisburg-Essen piush.aggarwal@stud.uni-due.de Ahmet Aker University of Duisburg-Essen a.aker@is.inf.uni-due.de Abstract Social media plays a great role in news dis- semination which includes good and bad news. However, studies show that news, in general, has a significant impact on our mental stature and that this influence is more in bad news. An ideal situation would be that we have a tool that can help to filter out the type of news we do not want to consume. In this paper, we provide the basis for such a tool. In our work, we focus on Twitter. We release a manually annotated dataset containing 6,853 tweets from 5 different topical categories. Each tweet is annotated with good and bad la- bels. We also investigate various machine learning systems and features and evaluate their performance on the newly generated dataset. We also perform a comparative analysis with sentiments showing that sen- timent alone is not enough to distinguish between good and bad news. 1 Introduction Social media sites like Twitter, Facebook, Reddit, etc. have become a major source of information seeking. They provide chances to users to shout to the world in search of vanity, attention or just shameless self-promotion. There is a lot of per- sonal discussions but at the same time, there is a base of useful knowledgeable content which is worthy enough to consider for the public inter- est. For example in Twitter, tweets may report about news related to recent events such as natural or man-made disasters, discoveries made, local or global election outcomes, health reports, financial updates, etc. In all cases, there are good and bad news scenarios. Studies show that news, in general, has a signif- icant impact on our mental stature (Johnston and Davey, 1997). However, it is also demonstrated that the influence of bad news is more significant than good news (Soroka, 2006; Baumeister et al., 2001) and that due to the natural negativity bias, as described by (Rozin and Royzman, 2001), hu- mans may end up consuming more bad than good news. Since bad news travels faster than good news (Kamins et al., 1997; Hansen et al., 2011) the consumption may increase. This is a real threat to the society as according to medical doctors and, psychologists exposure to bad news may have se- vere and long-lasting negative effects for our well being and lead to stress, anxiety, and depression (Johnston and Davey, 1997). (Milgrom, 1981; BRAUN et al., 1995; Conrad et al., 2002; Soroka, 2006) describe crucial role of good and bad news on financial markets. For instance, bad news about unemployment is likely to affect stock markets and in turn, the overall economy (Boyd et al., 2005). Differentiating between good and bad news may help readers to combat this issue and a system that filters news based on the content may enable them to control …

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