How can we expand an initial set of keywords with a target domain in mind? A possible application is to use the expanded set of words to search for specific information within the domain of interest. Here, we focus on online forums and specifically security forums. We propose IKEA, an iterative embedding-based approach to expand a set of keywords with a domain in mind. The novelty of our approach is three-fold: (a) we use two similarity expansions in the word-word and post-post spaces, (b) we use an iterative approach in each of these expansions, and (c) we provide a flexible ranking of the identified words to meet the user needs. We evaluate our method with data from three security forums that span five years of activity and the widely-used Fire benchmark. IKEA outperforms previous solutions by identifying more relevant keywords: it exhibits more than 0.82 MAP and 0.85 NDCG in a wide range of initial keyword sets. We see our approach as an essential building block in developing methods for harnessing the wealth of information available in online forums.

How are the sentiment and stance of online users affected by real-world events? Previous studies have ignored the role of events in co-determining sentiment and stance and hence have failed to understand the relationship between these two important aspects of public opinion. In this paper, we develop SentiStance, a systematic framework to understand the intertwined change of sentiment and stance due to real-world events in online discussions. In our approach: (a) we customize state-of-the-art NLP techniques to overcome domain-specific constraints, and (b) we provide an efficient way to quantify the change of sentiment and stance in tandem. As a case study, we focus on the 2020 United States Election events and we analyze 7.5 million posts from 4chan, Reddit, and Parler over a span of three months from November 2020 to January 2021. We showcase our framework by describing the effect that the Jan 6 insurrection had on concepts "Pence" and "Trump." Parler users turn significantly against Pence with (33.1% increase in Against stance and Negative sentiment), while Reddit users' opinion improves (with a drop of 7.1% in the same combination of sentiment and stance). By contrast, the effect of the same event on the concept "Trump" shows no statistically significant change. In addition, our results suggest that conditioning on significant events strengthens the correlation between sentiment and stance, which provides a new perspective on the debate around the correlation between sentiment and stance. Overall, we see our work as a fundamental building block towards a data-driven understanding of the interplay of preferences and emotions of online forum users towards a concept.

Online forums have been shown to contain a wealth of useful information. With a few notable exceptions, such forums have not received much attention from the research community, unlike other online social media. Our goal here is to conduct an in-depth thread-centric analysis of online forums, focusing on security forums. We propose, RThread, a comprehensive unsupervised clustering approach with a powerful visualization component, which we provide as a publicly-accessible web-based tool. Our approach leverages 92 thread features that span three groups: (a) temporal, (b) behavioral, and (c) content related. We analyze data from 8 security forums with 400k posts over a span of 8 years. First, we find that many thread-centric properties follow a log-normal distribution, which is persistent across several forums and over time. Second, we show how our approach can identify clusters of threads with similar behavior, while our visualization component provides an easy way to spot the differences between these clusters. Finally, we show how our approach can spot surprising behaviors, including a cluster, whose threads are used for Search Engine Optimization. We see our approach and our publicly available platform as a building block towards understanding forum activity and extracting interesting information in an unsupervised way.