Academic journal article Economic Inquiry

Forecasting with Social Media: Evidence from Tweets on Soccer Matches

Academic journal article Economic Inquiry

Forecasting with Social Media: Evidence from Tweets on Soccer Matches

Article excerpt

I. INTRODUCTION

Social media content--for example that produced on Twitter or Facebook--is increasingly used as a forecasting tool. For example, Hollywood studios use data from social media to forecast demand for new films. (1) Financial firms extract sentiment from Twitter to predict stock returns, and design funds to algorithmically trade based on this information. (2) And social media is now even used for economic forecasting: in 2012, the Australian Treasury department launched a division to harness social media data to forecast workforce participation and retail sentiment, among other things. (3)

But how useful and accurate a forecasting tool is social media? On one hand, social media content can harness the opinions and beliefs of a wide group of participants. Thus, the "wisdom of the crowd" (Galton 1907; Surowiecki 2005) may produce accurate forecasts. On the other hand, the incentives for an individual on social media to provide accurate information for forecasting may arguably be weak. Unlike in markets, accurate social media forecasts may enhance an individual's reputation, but are not directly profitable. And worse, there are many incidences of misinformation on social media. For example, a hoax Tweet on the Associated Press Twitter feed in 2013, misreporting an explosion at the White House in Washington, briefly wiped $136 billion off the S&P 500 Index. (4)

In this paper, we evaluate the accuracy of social media forecasting in a fast-moving, high-profile environment: English Premier League soccer matches. We study 13.8 million Tweets, an average of 5.2 Tweets per second, during 372 matches that took place during the 2013/2014 season. Our primary aim is to assess whether information contained in these Tweets can predict match outcomes. Furthermore, we also aim to assess whether the forecasting capacity of social media is concentrated during large events (such as the scoring of a goal or the issuance of a red card)--which would indicate that social media helps to "break" news--or whether any forecasting capacity is to be found in the aftermath of such events, in which case social media helps in the interpretation of information.

One problem is that social media content does not easily translate to probability forecasts. For example, we cannot state that X number of Tweets on a particular team, in a given interval, maps to a prediction that the team has a Y% chance of victory. Our solution, therefore, is to ask whether Twitter content can add information to probability forecasts produced by a prediction/betting market, Betfair. This is a high bar, as prediction markets have been found to outperform tipsters in the context of sports (Spann and Skiera 2009), and outperform polls and experts in the context of political races (Vaughan Williams and Reade 2016a). Prediction markets have even performed well when illiquid, as was the case in the corporate prediction markets studied by Cowgill and Zitzewitz (2015), and have also performed well when attempts have been made to manipulate prices, as was the case in the presidential betting markets studied by Rhode and Strumpf (2004) and Rothschild and Sethi (2016). In addition, and of particular relevance to our setting, prediction/betting markets have been found to accurately digest information on events (goals) almost immediately (Croxson and Reade 2014).

There are two broad theoretical reasons why Twitter activity might forecast outcomes even after controlling for betting market prices. First, there may be limitations to the accuracy and amount of information that finds its way into betting prices. A large literature in finance has documented the limits of arbitrage, whereby markets may be inefficient due to, for example, risk-aversion or borrowing constraints on the part of informed arbitrageurs (see Gromb and Vayanos 2010, for a survey). Social media, therefore, may provide an alternative repository for information that spectators do not have the risk appetite or resources to impound into betting prices. …

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