State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Exploiting Text and Network Context for Geolocation of Social Media Users

Research on automatically geolocating social media users has conventionally been based on the text content of posts from a given user or the social network of the user, with very little crossover between the two, and no bench-marking of the two approaches over compara- ble datasets. We bring the two threads of research together in first proposing a text-based method based on adaptive grids, followed by a hybrid network- and text-based method. Evaluating over three Twitter datasets, we show that the empirical difference between text- and network-based methods is not great, and that hybridisation of the two is superior to the component methods, especially in contexts where the user graph is not well connected. We achieve state-of-the-art results on all three datasets

Predicting Consumers’ Brand Sentiment Using Text Analysis on Reddit

With the emergence of data privacy regulations around the world (e.g. GDPR, CCPA), practitioners of Internet marketing, the largest digital marketing channel, face the trade-off between user data protection and advertisement targeting accuracy due to their current reliance on PII-related social media analytics. To address this challenge, this research proposes a predictive model for consumers’ brand sentiment based entirely on textual data from Reddit, i.e. fully compliant with current data privacy regulations. This author uses natural language processing techniques to process all post and comment data from the r/gadgets subreddit community in 2018 – extracting frequently-discussed brands and products through named entity recognition, as well as generating brand sentiment labels for active users in r/gadgets through sentiment analysis. This research then uses four supervised learning classifiers to predict brand sentiments for four brand clusters (Apple, Samsung, Microsoft and Google) based on the self-identified characteristics of Reddit users. Across all four brand clusters, the predictive model proposed by this research achieved a ROC AUC score above 0.7 (three out of the four above 0.8). This research thus shows the predictive power of self-identified user characteristics on brand sentiments and offers a non-PII-required consumer targeting model for digital marketing practitioners