The diffusion of misinformation on social media: Temporal pattern, message, and source

Highlights

• False political rumors tend to resurface multiple times after the initial publication.

• False political rumors often turn into a more intense and extreme version over time.

• Resurged old political rumors tend to be presented as “news”.

• None-traditional partisan media are often behind the constant generation of false “news”.

Abstract

This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temporal pattern, content mutation, and sources of misinformation. We traced the lifecycle of 17 popular political rumors that circulated on Twitter over 13 months during the 2012 U.S. presidential election. Using text analysis based on time series, we found that while false rumors (misinformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension around the target dissolves. We observed that rumors resurface by partisan news websites that repackage the old rumor into news and, gain visibility by influential Twitter users who introduce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mechanism by which these messages evolve over time.

Introduction

Prevalent misinformation online is a growing concern around the globe (WEF, 2014). Whether it is in the form of conspiracy theories or unsubstantiated rumors, false information is now a part of the contemporary media system where varying degrees of information sources vie for our attention. In particular, “fake news,” which generally refers to fabricated news stories purporting to be true, came to the forefront in 2016, circulating wildly during the Brexit vote and the American presidential election. As a result, the Oxford English Dictionary named “post-truth” the 2016 word of the year to highlight less influential role of objective truth in shaping public opinions than political belief or emotion.

There has also been considerable research on this topic. Previous research investigated the effects of exposure to false information and corrections on attitudes and political behavior (Berinsky, 2017; Bode & Vraga, 2015; Cacciatore et al., 2014; Fridkin, Kenney, & Wintersieck, 2015; Garrett, 2011, Nyhan and Reifler, 2015, Uscinski et al., 2016, Weeks and Garrett, 2014, Wood and Porter, 2016). In general, these studies have found that individuals are more likely to believe in dubious statements that match their partisanship than statements that run counter to their belief (e.g., Weeks, 2015). In addition, some studies reported that corrections usually work in experimental settings where individuals are required to read random debunking messages (e.g., Nyhan & Reifler, 2015), although such efficacy was challenged in a social media environment where people selectively share corrective messages (Shin & Thorson, 2017).

Despite growing research in rumors and misinformation, what is largely missing from the current work is dynamic analysis of misinformation diffusion processes online. Scholars argue that misinformation gains its power when it is repeated and passed along from one person to another (DiFonzo & Bordia, 2007). That is, the defining characteristics of misinformation are its dynamic mode and collective process that unfolds over time. Therefore, unlike previous studies that examined misinformation as static communication and that take snapshots from experiments or surveys, we focus on changing communicative patterns that occur during the lifecycle of misinformation on social media. Largely exploratory in nature, this study examines a set of questions that shed new light on the nature of political rumoring – and diffusion of information broadly.

Our study differs from previous research in the field of computer science and engineering (Bessi et al., 2015, Friggeri et al., 2014, Kwon et al., 2014; Vicario et al., 2016), which places relatively less emphasis on understanding the social psychological context underlying the phenomenon. In addition, these previous studies tend to treat misinformation as if the diffusing message is a fixed object. On the other hand, our study takes an alternative perspective, which views misinformation to be mutable and malleable as they diffuse. We explore this idea using a multiple-case study approach, while paying attention to the distinct context of each rumor. Our research builds on studies (Allport and Postman, 1947, Rojecki and Meraz, 2016) that exceptionally focused on dynamic communication process in the rumoring phenomenon. For instance, Rojecki and Meraz (2016) investigated the agenda setting power among webpages, Google searches, and media coverage with two rumor cases. Another classic study of rumor, conducted by Allport and Postman (1947), examined how rumor content changes in a serial transmission chain in which a story is passed along from one person to another.

Our study aims at investigating misinformation diffusion as an evolving phenomenon focusing on three components: temporal pattern, rumor narrative, and rumor sources. To achieve this goal, we employ various time series analysis on 17 political rumors that circulated on Twitter over 13 months during the 2012 U.S. election period. The context of the 2012 election on Twitter is still relevant today. First, Twitter emerged as a primary political communication channel during the 2012 election and still remains to be prominent (Conway, Kenski, & Wang, 2015). Second, political misinformation circulating on social media gained attention as a serious threat to democracy during 2012, and lately the phenomenon has become the center of public discussion (Ehrenberg, 2012, Shin et al., 2016). Through our analyses, we show that many contested rumors resurface by partisan websites that repackage old rumors into “news”, and gain visibility by influential Twitter users who share such content with their followers. We also show that rumor resurgence often accompanies changes in content, generally in the direction of exaggeration, although this trend abruptly stops when the election is over. In this paper, we argue that digital media scholars should consider the mutability of diffusing content and the mechanism by which messages change over time. We also highlight the underlying partisan media users’ motivations and strategies that drive the evolution of political misinformation.