Sourcing and Automation of Political News and Information During Three European Elections

Abstract

Voters increasingly rely on social media for news and information about politics. But increasingly, social media has emerged as a fertile soil for deliberately produced misinformation campaigns, conspiracy, and extremist alternative media. How does the sourcing of political news and information define contemporary political communication in different countries in Europe? To understand what users are sharing in their political communication, we analyzed large volumes of political conversation over a major social media platform—in real-time and native languages during campaign periods—for three major European elections. Rather than chasing a definition of what has come to be known as “fake news,” we produce a grounded typology of what users actually shared and apply rigorous coding and content analysis to define the types of sources, compare them in context with known forms of political news and information, and contrast their circulation patterns in France, the United Kingdom, and Germany. Based on this analysis, we offer a definition of “junk news” that refers to deliberately produced misleading, deceptive, and incorrect propaganda purporting to be real news. In the first multilingual, cross-national comparison of junk news sourcing and consumption over social media, we analyze over 4 million tweets from three elections and find that (1) users across Europe shared substantial amounts of junk news in varying qualities and quantities, (2) amplifier accounts drive low to medium levels of traffic and news sharing, and (3) Europeans still share large amounts of professionally produced information from media outlets, but other traditional sources of political information including political parties and government agencies are in decline.

Resilience to Online Disinformation: A Framework for Cross-National Comparative Research

“…At the same time, however, the data show great country variation with citizens in many Western and Northern European countries (e.g., Germany, Denmark, and the Netherlands) reporting low levels of exposure to online disinformation. Moreover, citizens in those countries are less willing to disseminate disinformation on social networks (Neudert et al 2019).…”

Sourcing and Automation of Political News and Information over Social Media in the United States, 2016-2018

“…While the presidential election captures political communication over an extended period of time, the debate surrounding the SOTU address is traditionally more momentous and issue based. We tested and developed the methodological approach and typology described below in several elections in Europe between 2016 and 2018 (Neudert, Howard, & Kollanyi, 2019).…”

“…Overall, this process allowed the team to develop a comprehensive typology of sources of political news and information, described below, involving over 1,000 hours of coding, six training sessions, and biweekly review meetings. The typology reflects 22 months of iterative coding procedures, and during this time the typology was exported to other country contexts: France, the UK, Germany, Mexico, Sweden, and Brazil (Glowacki et al, 2018;Hedman et al, 2018;Machado, Kira, Hirsch, & Marchal, 2018;Neudert et al, 2019).…”

Mobile phones in the spread of unreliable information on Twitter: evidence from the 2017 French presidential campaign

“…The spread of disinformation and so-called “fake news” has emerged as a prominent public issue, especially in Europe (Neudert et al, 2019). Despite numerous references to “fake news” in the general media, the notion is overused and often misused (Tandoc et al, 2018).…”

“…Despite numerous references to “fake news” in the general media, the notion is overused and often misused (Tandoc et al, 2018). Faced with the diversity of uses of the “fake news” tag, scholars have identified the methodological challenges of using such terminology and have developed a precise typology for studying the spread of “junk news” (Neudert et al, 2019). This typology is based on the domain names of information sources shared on Twitter, and is used to code, among other things, the professionalism and degree to which such sources are highly biased or ideologically driven.…”