False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

Quantifying high-frequency market reactions to real-time news sentiment announcements

We examine intra-day market reactions to news in stock-specific sentiment disclosures. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we extract information on the relevance as well as the direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model using 20 second intervals. Analyzing a cross-section of stocks traded at the London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However, this is only true if news items are classified as highly relevant. Liquidity supply reacts less distinctly due to a stronger influence of idiosyncratic noise. Furthermore, evidence for abnormal highfrequency returns after news in sentiments is shown. JEL-Classification: G14, C3

Stock market interactions and the impact of macroeconomic news – evidence from high frequency data of European futures markets

This study analyzes the short-term dynamic spillovers between the futures returns on the DAX, the DJ Eurostoxx 50 and the FTSE 100. It also examines whether economic news is one source of international stock return co-movements. In particular, we test whether stock market interdependencies are attributable to reactions of foreign traders to public economic information. Moreover, we analyze whether cross-market linkages remain the same or whether they do increase during periods in which economic news is released in one of the countries. Our main results can be summarized as follows: (i) there are clear short term international dynamic interactions among the European stock futures markets; (ii) foreign economic news affects domestic returns; (iii) futures returns adjust to news immediately; (iv) announcement timing of macroeconomic news matters; (v) stock market dynamic interactions do not increase at the time of the release of economic news; (vi) foreign investors react to the content of the news itself more than to the response of the domestic market to the national news; and (vii) contemporaneous correlation between futures returns changes at the time of macroeconomic releases. JEL Classification: G14, G1