Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics

Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.Comment: 3rd International Conference on Knowledge Engineering and Applications (ICKEA 2018) - Moscow, Russia (June 25-27 2018

Search strategies of Wikipedia readers

The quest for information is one of the most common activity of human beings. Despite the the impressive progress of search engines, not to miss the needed piece of information could be still very tough, as well as to acquire specific competences and knowledge by shaping and following the proper learning paths. Indeed, the need to find sensible paths in information networks is one of the biggest challenges of our societies and, to effectively address it, it is important to investigate the strategies adopted by human users to cope with the cognitive bottleneck of finding their way in a growing sea of information. Here we focus on the case of Wikipedia and investigate a recently released dataset about users’ click on the English Wikipedia, namely the English Wikipedia Clickstream. We perform a semantically charged analysis to uncover the general patterns followed by information seekers in the multi-dimensional space of Wikipedia topics/categories. We discover the existence of well defined strategies in which users tend to start from very general, i.e., semantically broad, pages and progressively narrow down the scope of their navigation, while keeping a growing semantic coherence. This is unlike strategies associated to tasks with predefined search goals, namely the case of the Wikispeedia game. In this case users first move from the ‘particular’ to the ‘universal’ before focusing down again to the required target. The clear picture offered here represents a very important stepping stone towards a better design of information networks and recommendation strategies, as well as the construction of radically new learning paths

Year 2 Final Report: Project Performance Reporting July 1, 2021- June 20, 2022

This project examines messaging strategies on publicly accessible microblogs (e.g., Twitter) used by extremist ideological groups. Our objective is to provide Department of Homeland Security (DHS) decision-makers and associated partners with insights about processes extreme ideological groups use to recruit members, harness social identities, mobilize communication around issues, increase commitment to extremism, and incite violent action. We analyze digital traces (e.g., websites, microblog archives) and conduct controlled, randomized experiments to understand how messaging content and strategies foreshadow extreme cognitions, affect, and behaviors. Key insights from our analyses have uncovered the following insights: Key Findings from Digital Trace Results • Rise in religious rhetoric on microblogs preceded violent events. We observed this phenomenon across multiple jihadist attacks. A similar, though more muted, rise in religious rhetoric preceded the Jan 6 Capitol riots. • Violent ideological groups use appeals to social identity along with language that justifies the group’s stances and emphasizes differences with outgroups. Non-violent groups use appeals to social identity along with language that focuses on group agency, future possibilities, and is more hesitant. Implications: These findings provide important signals for analysts monitoring rhetoric from known and emerging ideological groups that mark escalation toward extremism and violence. Findings also identify key language differences between non-violent and violent group

Analyzing Polarization on Social Media: A Case Study of the 2022 Brazil Presidential Election

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceSocial Media has become a big part of our society and has now a significant role in the relationships between inter and intra-communities. Twitter is now an important communication platform for political campaigns: in the last years, politicians, campaigners, and general users have been extensively using Twitter to promote campaigns and engage in political discussions. Some studies argue that social media can create filter bubbles by limiting the flow of online information, and therefore creating communities where exposure to political diversity is rare. This selective exposure can build echo chambers where individuals only interact with those who have the same opinions as they have and by doing that, they build a polarized community. Identifying, understanding, and mitigating polarization is very important for the democratic process. People should be exposed to different ideas and opinions so they can choose their representatives without being influenced by some portion of the information. This project analyzed political polarization on social media using data from Twitter. Brazil’s presidential election in 2022 was used as a case study. Tweets from the two main candidates were extracted. A Topic Modeling algorithm was used to cluster tweets in topics. An Engagement Graph was built based on the interactions between users, candidates, and topics and was used to compute the Topic Centrality measures. A pre-trained Sentiment Analysis model was used to measure the sentiment polarity of each tweet. In the end, the project analyzed the extracted features and identified which topics were more central to each candidate and how users interact with them. The major conclusion of this work is that polarization in Brazil is more affective than ideological since the user’s sentiments towards topics are not as relevant as the sentiments towards the candidates