Real-time detection of content polluters in partially observable Twitter networks

9th International Workshop on Modeling Social Media (MSM 2018) Applying Machine Learning and AI for Modeling Social Media.Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset.Mehwish Nasim, Andrew Nguyen, Nick Lothian, Robert Cope, Lewis Mitchel

Understanding call logs of smartphone users for making future calls

In this measurement study, we analyze whether mobile phone users exhibit temporal regularity in their mobile communication. To this end, we collected a mobile phone usage dataset from a developing country - Pakistan. The data consists of 783 users and 229, 450 communication events. We found a number of interesting patterns both at the aggregate level and at dyadic level in the data. Some interesting results include: the number of calls to different alters consistently follow the rank-size rule; a communication event between an ego-alter(user-contact) pair greatly increases the chances of another communication event; certain ego-alter pairs tend to communicate more over weekends; ego-alter pairs exhibit autocorrelation in various time quantum. Identifying such idiosyncrasies in the ego-alter communication can help improve the calling experience of smartphone users by automatically (smartly) sorting the call log without any manual intervention.Mehwish Nasim, Aimal Rextin, Numair Khan, and Muhammad Muddassir Mali