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 conversations, 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 game start, allowing for bets informed by its predictions. We first discuss the rationale behind our approach, then 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 (10 sample), and real-time Twitter data (full stream) collected by monitoring the conversations about all soccer matches of the four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between October, 25th 2014 and November, 26th 2014.
Various works called for caution when using social media to predict exogenous events @cite_9 @cite_13 : in such cases, it is important to keep in mind that the usage of machine learning algorithms or statistical models that function as black boxes can yield to results which are not interpretable and misleading @cite_22 . For these reasons, when we designed our machine learning framework we based it on simple assumptions: the prediction dynamics are entirely explainable and observable in real time. In fact, our model relies only on one single feature (the average conversation sentiment measured over time) and it allows to interpret the predictions in a concise and clear way. Our hypotheses are also rooted on recent advances in social psychology that support the idea that collective attention enhances group emotions @cite_28 @cite_25 @cite_14 .
@cite_14 @cite_22 @cite_28 @cite_9 @cite_13 @cite_25
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The idea that group contexts can intensify emotions is centuries old. Yet, evidence that speaks to how, or if, emotions become more intense in groups remains elusive. Here we examine the novel possibility that group attention—the experience of simultaneous coattention with one’s group members—increases emotional intensity relative to attending alone, coattending with strangers, or attending nonsimultaneously with one’s group members. In Study 1, scary advertisements felt scarier under group attention. In Study 2, group attention intensified feelings of sadness to negative images, and feelings of happiness to positive images. In Study 3, group attention during a video depicting homelessness led to greater sadness that prompted larger donations to charities benefiting the homeless. In Studies 4 and 5, group attention increased the amount of cognitive resources allocated toward sad and amusing videos (as indexed by the percentage of thoughts referencing video content), leading to more sadness and happiness, respectively. In all, these effects could not be explained by differences in physiological arousal, emotional contagion, or vicarious emotional experience. Greater fear, gloom, and glee can thus result from group attention to scary, sad, and happy events, respectively. In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error? Social psychologists have studied the psychological processes involved in persuasion, conformity, and other forms of social influence, but they have rarely modeled the ways influence processes play out when multiple sources and multiple targets of influence interact over time. However, workers in other fields from sociology and economics to cognitive science and physics have recognized the importance of social influence and have developed models of influence flow in populations and groups—generally without relying on detailed social psychological findings. This article reviews models of social influence from a number of fields, categorizing them using four conceptual dimensions to delineate the universe of possible models. The goal is to encourage interdisciplinary collaborations to build models that incorporate the detailed, microlevel understanding of influence processes derived from focused laboratory studies but contextualized in ways that recognize how multidirectional, dynamic influences are situate... Predicting X from Twitter is a popular fad within the Twitter research subculture. It seems both appealing and relatively easy. Among such studies, electoral prediction is maybe the most attractive, and a growing body of literature exists on this topic. This research problem isn't only interesting, but is also extremely difficult. However, most authors seem to be more interested in claiming positive results than in providing sound and reproducible methods. It's also especially worrisome that recent papers seem to only acknowledge those studies supporting the idea that Twitter can predict elections. This is all problematic because while simple approaches are purported to be good enough, the predictive power of Twitter regarding elections has been greatly exaggerated, and difficult research problems still lie ahead. Using social media for political discourse is becoming common practice, especially around election time. One interesting aspect of this trend is the possibility of pulsing the public’s opinion about the elections, and that has attracted the interest of many researchers and the press. Allegedly, predicting electoral outcomes from social media data can be feasible and even simple. Positive results have been reported, but without an analysis on what principle enables them. Our work puts to test the purported predictive power of socialmedia metrics against the 2010 US congressional elections. Here, we applied techniques that had reportedly led to positive election predictions in the past, on the Twitter data collected from the 2010 US congressional elections. Unfortunately, we find no correlation between the analysis results and the electoral outcomes, contradicting previous reports. Observing that 80 years of polling research would support our findings, we argue that one should not be accepting predictions about events using social media data as a black box. Instead, scholarly research should be accompanied by a model explaining the predictive power of social media, when there is one. Across disciplines, social learning research has been unified by the principle that people learn new behaviors to the extent that they identify with the actor modeling them. We propose that this conceptualization may overlook the power of the interpersonal situation in which the modeled behavior is observed. Specifically, we predict that contexts characterized by shared in-group attention are particularly conducive to social learning. In two studies, participants were shown the same written exchange in either paragraph or chat form across multiple interpersonal contexts. We gauged social learning based on participants’ tendency to imitate the form of the written exchange to which they were exposed. Across both studies, results reveal that imitation is especially likely among individuals placed in the specific context of simultaneous observation with a similar other. These findings suggest that shared in-group attention is uniquely adaptive for social learning.