Predicting Tomorrow’s Headline using Twitter Deliberations Roshni Chakraborty IIT Patna India roshni.pcs15@iitp.ac.in Abhijeet Kharat IIT Patna India abhijeet.mtcs17@iitp.ac.in Apalak Khatua XLRI Jamshedpur India apalak@xlri.ac.in Sourav Kumar Dandapat IIT Patna, India sourav@iitp.ac.in Joydeep Chandra IIT Patna, India joydeep@iitp.ac.in Abstract Predicting the popularity of a news article is a challenging task. Existing literature mostly focused on article contents and polarity to predict the popularity. However, existing re- search has not considered the users prefer- ence towards a particular article. Understand- ing users preference is an important aspect for predicting the popularity of news articles. Hence, we consider social media data, from the Twitter platform, to address this research gap. In our proposed model, we have con- sidered the users involvement as well as the users reaction towards an article to predict the popularity of the article. In short, we are predicting tomorrows headline by probing to- days Twitter discussion. We have considered 300 political news articles from the New York Post, and our proposed approach has outper- formed other baseline models. 1 Introduction Gone are those days when an office going New Yorker used to board the subway with a folded newspaper in his hand. Reading morning newspapers on New Yorks subway is becoming outdated. Things have changed drastically in recent times. Todays millennial genera- tion is not only emotionally but also physically tied to their smartphones and tablets. This has severely af- fected the newspaper industry. All leading newspapers across the globe have reported a sharp drop in their print circulation. So, the future of this industry lies on the digital platform. The competition in this newspa- per industry is not anymore about sending the print version to the remotest corner of the country. The challenge of this digital platform is to understand the latent psychological aspects of the users. If a newspa- per fails to satisfy the user, then within the next few seconds she will switch to another news-related app. This will directly impact the ad revenue of a news outlet. Between various news related apps, and vari- ous social media platforms, users these days are spoilt for choice. Customer loyalty is a concept of a bygone era in this digital age, and the customers preferences are also not homogeneous. In brief, the phenomenal growth of online news consumption and innumerable news sources has significantly increased the competi- tion among news media outlets. Further, the contin- uous influx of newsworthy events further aggravates the situation. Thus, for media outlets, the need of the hour is to develop an automated system that can help them to predict which of the todays headlines will maintain its popularity tomorrow. Existing literature has attempted to address this. However, this stream of research mostly explored var- ious features and contents of the articles and the ti- tle of the articles [FVC15, LWZ + 17]. Prior stud- ies considered the subjectivity and polarity of con- tents [FVC15, KWHR16], the sentiment of the head- Copyright © CIKM 2018 for the individual papers by the papers' authors. Copyright © CIKM 2018 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).