Deep Learning for Geolocating Social Media Users and Detecting Fake News

Social networks like Facebook and Twitter have become a great tool for connecting people, communicating and disseminating ideas. While these platforms are obviously beneficial to our society, concerns are growing with respect to spreading fake news. Such untrustworthy information is likely to create adverse impact on the public opinion, leading to uncertain outcomes of public events such as elections. However, the process of identifying fake news is time-consuming, laborious, and requires expert knowledge. On top of that, geolocating users on social media platforms is a very useful as it enables applications including event detection and social unrest forecasting. Nonetheless, not many users disclose their location via their profile, or the provided information is often unreliable. As such, news verification and user geo-location on social networks are two important, interconnected big data problems. Machine learning solutions have recently been proposed to address these problems. Most of the existing solutions focus on engineering features extracted from content, social users and information propagation patterns. In this work, we will present novel solutions to fake news detection and user geolocation using deep neural networks. The results from the experiments conducted on several real-world datasets show that our methods outperform the existing approaches. 1.0 INTRODUCTION Over the last decade, social media platforms like Twitter and Facebook have become a very important tool for connecting people. Being able to provide open and quick access to a sheer amount of information, social networks have attracted hundreds of millions of users across the globe. According to [1], Facebook has more than 2.2B active users by March 2018, while the number of active users on Twitter is more than 330M. The information on social networks is user-generated; therefore, it is greatly diverse. People talk about everything via social posts, and the posts could be shared or republished. Using this mechanism, the information can be propagated widely in a short time. For this reason, social media platforms can be seen as human sensing networks. Among social media platforms, Twitter seems to be the most attractive for researchers because of its open data application programming interfaces (APIs). On Twitter, users publish short text messages, known as tweets. Tweets can be re-published by other users, which are called retweets. These messages and user profiles can be easily collected via the Twitter API; as such, a lot of popular datasets for social network analysis research are collected from Twitter. Unverified information like rumors or fake news find social networks a breeding ground. Fake news, intentionally written for financial incentive, politic influence or other purposes is very difficult to detect. It often requires expert knowledge to judge a meticulously manipulated piece of misleading news. Therefore, online users, who usually just skim posts on social networks, are likely to spread fake news. Fake news is believed to be a severe social problem because it causes harm not only to a single person but also to societies and countries. More precisely, fake news might have adverse effect on finance of giant corporations, social inter-class contradictions, propagating hatred and distrust among citizens. As an example, in September Deep Learning for Geolocating Social Media Users and Detecting Fake News S3-5 2 STO-MP-IST-16