The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events

Social Media’s impact on Intellectual Property Rights

This is a draft chapter. The final version is available in Handbook of Research on Counterfeiting and Illicit Trade, edited by Peggy E. Chaudhry, published in 2017 by Edward Elgar Publishing Ltd, https://doi.org/10.4337/9781785366451. This material is for private use only, and cannot be used for any other purpose without further permission of the publisher.Peer reviewe

Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump

Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population. Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to develop an in-domain training set approaching 1 million tweets. We investigate the social networks formed by the interactions among millions of Twitter users and infer the support of each user to the presidential candidates. The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy. Moreover, the Twitter opinion trend precedes the aggregated NYT polls by 10 days, showing that Twitter can be an early signal of global opinion trends. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Adversarial behaviours knowledge area

The technological advancements witnessed by our society in recent decades have brought improvements in our quality of life, but they have also created a number of opportunities for attackers to cause harm. Before the Internet revolution, most crime and malicious activity generally required a victim and a perpetrator to come into physical contact, and this limited the reach that malicious parties had. Technology has removed the need for physical contact to perform many types of crime, and now attackers can reach victims anywhere in the world, as long as they are connected to the Internet. This has revolutionised the characteristics of crime and warfare, allowing operations that would not have been possible before. In this document, we provide an overview of the malicious operations that are happening on the Internet today. We first provide a taxonomy of malicious activities based on the attacker’s motivations and capabilities, and then move on to the technological and human elements that adversaries require to run a successful operation. We then discuss a number of frameworks that have been proposed to model malicious operations. Since adversarial behaviours are not a purely technical topic, we draw from research in a number of fields (computer science, criminology, war studies). While doing this, we discuss how these frameworks can be used by researchers and practitioners to develop effective mitigations against malicious online operations.Published versio