Fake News Detection in Social Networks via Crowd Signals

Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users' flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party fact-checking organization), and stop the spread of news identified as fake by an expert. The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network. It is especially challenging to achieve this objective as it requires detecting fake news with high-confidence as quickly as possible. We show that in order to leverage users' flags efficiently, it is crucial to learn about users' flagging accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian inference for detecting fake news and jointly learns about users' flagging accuracy over time. Our algorithm employs posterior sampling to actively trade off exploitation (selecting news that maximize the objective value at a given epoch) and exploration (selecting news that maximize the value of information towards learning about users' flagging accuracy). We demonstrate the effectiveness of our approach via extensive experiments and show the power of leveraging community signals for fake news detection

On predictability of rare events leveraging social media: a machine learning perspective

Information extracted from social media streams has been leveraged to forecast the outcome of a large number of real-world events, from political elections to stock market fluctuations. An increasing amount of studies demonstrates how the analysis of social media conversations provides cheap access to the wisdom of the crowd. However, extents and contexts in which such forecasting power can be effectively leveraged are still unverified at least in a systematic way. It is also unclear how social-media-based predictions compare to those based on alternative information sources. To address these issues, here we develop a machine learning framework that leverages social media streams to automatically identify and predict the outcomes of soccer matches. We focus in particular on matches in which at least one of the possible outcomes is deemed as highly unlikely by professional bookmakers. We argue that sport events offer a systematic approach for testing the predictive power of social media, and allow to compare such power against the rigorous baselines set by external sources. Despite such strict baselines, our framework yields above 8% marginal profit when used to inform simple betting strategies. The system is based on real-time sentiment analysis and exploits data collected immediately before the games, allowing for informed bets. We discuss the rationale behind our approach, describe the learning framework, its prediction performance and the return it provides as compared to a set of betting strategies. To test our framework we use both historical Twitter data from the 2014 FIFA World Cup games, and real-time Twitter data collected by monitoring the conversations about all soccer matches of four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between Oct. 25th 2014 and Nov. 26th 2014.Comment: 10 pages, 10 tables, 8 figure

Leveraging the Wisdom of the Crowd to Address Societal Challenges: Revisiting the Knowledge Reuse for Innovation Process through Analytics

Societal challenges can be addressed not only by experts but also by crowds. Crowdsourcing provides a way to engage a crowd to contribute to the solutions of some of the biggest challenges of our era: how to cut our carbon footprint, how to address worldwide epidemic of chronic disease, and how to achieve sustainable development. Isolated crowd-based solutions in online communities are not always creative and innovative. Hence, remixing has been developed as a way to enable idea evolution and integration, and to harness reusable innovative solutions. Understanding the generativity of remixing is essential to leveraging the wisdom of the crowd to solve societal challenges. At its best, remixing can promote online community engagement, as well as support comprehensive and innovative solution generation. Organizers can maintain an active online community, community members can collectively innovate and learn, and, as a result, society can find new ways to solve important problems. We address what affects the generativity of a remix by revisiting the knowledge reuse for innovation process model. We analyze the reuse of proposals in Climate CoLab, an online innovation community that aims to address global climate change issues. Our application of several analytical methods to study factors that may contribute to the generativity of a remix reveals that remixes that include prevalent topics and integration metaknowledge are more generative. We conclude by suggesting strategies and tools that can help online communities better harness collective intelligence for addressing societal challenges

Equity crowdfunding, shareholder structures, and firm performance

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Research question/issue: This paper provides a first-time glimpse into the postcampaign financial and innovative performance of equity-crowdfunded (ECF) and matched nonequity-crowdfunded (NECF) firms. We further investigate how direct and nominee shareholder structures in ECF firms are associated with firm performance. Research findings/insights: We find that ECF firms have 8.5 times higher failure rates than matched NECF firms. However, 3.4 times more ECF firms have patent applications than matched NECF firms. Within the group of ECF firms, we find that ECF firms financed through a nominee structure make smaller losses, whereas ECF firms financed through a direct shareholder structure have more new patent applications, including foreign patent applications. Theoretical/academic implications: Our findings suggest that there are important adverse selection issues on equity crowdfunding platforms, although these platforms also serve as a catalyst for innovative activities. Moreover, our findings suggest that there is a more complex relationship between dispersed versus concentrated crowd shareholders and firm performance than currently assumed in the literature. Practitioner/policy implications: For policy makers and crowdfunding platforms, investor protection against adverse selection will be important to ensure the sustainability of equity crowdfunding markets. For entrepreneurs and crowd investors, our study highlights how equity crowdfunding and the adopted shareholder structure relate to short-term firm performance.Research Foundation—Flander