It forms part of a wider landscape of information operations conducted by governments and other entities [2, 3]. The key distinction is the intent of the spreader. Dean Jacksons November 2017 Q&A with Jonathon Morgan on Tracking Digital Disinformation., Dean Jacksons July 2017 Q&A with Phil Howard on Computational Propagandas Challenge to Democracy., Image Credit: Alexander Sviridov/Shutterstock. In practical terms, this suggests that any interventions at the information operation level should be targeted at specific segments who actually share the material rather than the entire population. And you may no longer be able to have conversations with that person. While bot networks and coordinated inauthentic activity are important, everyday human behaviour may actually account for a great deal of the spread of false material (Vosoughi, Roy and Aral, 2018). A comprehensive anti-disinformation strategy should probably consider deliberate onward sharers, accidental onward sharers, and ultimate recipients (the wider audience), as well as the initial originators of the material. Are there gender-linked individual differences that influence the behaviour? These checks and exclusions were carried out prior to any data analysis. Other variables were included in the analysis on an exploratory basis with no specific hypotheses being advanced. The methodology exactly replicated that of Study 1, except in the case of details noted below. Talwar et al., 2019; Pennycook and Rand, 2019; Pennycook, Cannon and Rand, 2018; Celliers and Hattingh, 2020; Anthony and Moulding, 2019). The answers to those questions will help inform the development of effective interventions. Answer (1 of 11): Most of the dis/misinformation spread these days is actually accomplished by subscribers to social media. Similarly, demographic variables particularly being young and male are relevant to sharing disinformation. Treating all sharers and indeed recipients of false information as if they are the same would be a mistake. Yes, everyone made choices as they started falling for a disinformation campaign. Authoritativeness and Consensus conditions coded as dummy variables; higher = 1, lower = 0 in each case. Alongside other recent work [43,44], the current findings suggest that repeated exposure to disinformation materials may increase our likelihood of sharing it, even if we dont believe it. Even a partial list of elections where Russian-produced or -supported disinformation has featured includes the French, German, and American elections in 2016 and 2017; the 2018 Czech presidential election; and the 2017 vote on Catalonian secession from Spain. Policy Brief: Why Do People Share Disinformation On Social Media? mid the coronavirus disease 2019 (COVID-19) pandemic, foreign state actors have been spreading disinformation on social media about the disease and the virus that causes it (Bright et al. As a consequence, the 6069 age group is itself over-represented in this sample compared to the broader US population. While there are likely to be a number of other variables that also influence the spread of disinformation, there are grounds for believing that consistency, consensus and authority may be important. However, pilot work (see Study 1, Study 4) with comparable samples indicated that people did see the sources as differing in authoritativeness. Fortunately, research also hints at solutionsthough . The fact that only a minority of people actually propagate disinformation makes it important to consider what sets them apart from people who don't spread untrue material further. Straight off the top, it would be incautious to conclude that Russia's pullback across the Dnieper represents a dramatic turn in the course of the Ukraine conflict. Psychological research suggests that once our minds are made up on important matters, changing them can be as difficult as stopping a train hurtling at full speed, even when there's danger straight ahead. In the 21st century security environment, information has become a key instrument of national power. However, it is possible that at least some people know the material is untrue, and they spread it anyway. However, in line with hypothesis 3, higher levels of conservatism were associated with higher likelihood of sharing disinformation. 102 out of 674 participants (15.1%) indicated that they had out ever shared a political news story online that they later found out was made up, while 42 out of 674 indicated they had shared one that they thought AT THE TIME was made up (6.2%). Federal government websites often end in .gov or .mil. For example, one could measure digital literacy in a sample of respondents, then do analyses of their past social media sharing behaviour. If this finding is correct, it could be due to the individuals who choose to share false material on purpose: higher digital media literacy levels could be an enabling factor in that behaviour. Participant demographics are shown in Table 1 (column 2). One possibility is that the sample characteristics were different (this sample was younger, better educated, and drawn from a different source). But he did not act alone. Cheap barrier to entry Higher levels of Extraversion (a new finding) and lower ages (as in Study 3) were associated with higher reported likelihood of sharing the stimuli. The lower authoritativeness group were slight variants on real usernames that had previously retweeted either stories from Infowars.com or another story known to be untrue. The main dependent variable, likelihood of sharing, had a very skewed distribution with a strong floor effect: 39.4% of the participants indicated they were very unlikely to share any of the three stories they saw. Authoritativeness and Consensus conditions coded as dummy variables; higher = 1, lower = 0 in each case. For the main analysis, Study 2 replicates a number of key findings from Study 1. Of the three stimuli selected for use in this study, a one-sample t-test showed that the least right-wing was statistically significantly higher than the midpoint, (t(39) = 4.385, p < .001, d = 0.70). Ordinary people may propagate the material to their own social networks through deliberate sharinga core function of platforms such as Facebook and Twitter. Is it done with good intentions, or maliciously? Again, the pattern of results emerging from Study 4 had some similarities but also some differences from Studies 13. The people who subsequently see it may then share it to their own social networks, potentially leading to an exponential rise in its visibility. For the focal analysis in this study, the sample size conferred 94.6% power to detect R2 = .04 in a multiple regression with 15 predictors (2-tailed, alpha = .05). A key issue is the distributions of the main outcome variables, which were heavily skewed with strong floor effects. They completed demographic items; a five-factor personality test (measuring extraversion, agreeableness, conscientiousness, neuroticism, openness to experience); a measure of political orientation (conservatism); and a measure of digital media literacy. They then rated the likelihood of them sharing the post to their own public timeline, on an 11-point scale anchored at Very Unlikely and Very Likely. Dean Jacksons February 2018 Q&A with Dipayan Ghosh on the Commercial Drivers of Precision Propaganda., Dean Jacksons February 2018 Q&A with Maria Ressa on Digital Disinformation and Philippine Democracy in the Balance.. For the focal analysis in this study, the sample size conferred 92.6% power to detect R2 = .04 in a multiple regression with 15 predictors (2-tailed, alpha = .05). The people reporting the greatest likelihood of sharing disinformation were those who thought it likely to be true, or who had pre-existing attitudes consistent with it. Having unknowingly shared untrue material (Table 13) was significantly predicted by higher Extraversion, lower Conscientiousness and male gender. The https:// ensures that you are connecting to the Study 1 also examined predictors of reported historical sharing of false political information. Authoritativeness and Consensus conditions coded as dummy variables; higher = 1, lower = 0 in each case. An explanation advanced for this is that they may have lower levels of digital media literacy and are thus less able to detect a message as being untruthful, particularly when it appears to come from an authoritative or trusted source. Consumers do not necessarily need to be persuaded by these storiesthe introduction of doubt or anxiety may be enough to inspire distrust or political disengagement. The spread of low-credibility content by social bots. This personalisation relies on intrusive tracking of your behaviours online. Implementing a Five-Factor Personality Inventory for Use on the Internet, European Journal of Psychological Assessment, Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO FFI): Professional Manual, The 12 item Social and Economic Conservatism Scale (SECS). 1]. Research participants have been found to report a greater likelihood of propagating a social media message if it came from a trustworthy source [24]. While this would be more useful to those seeking to spread disinformation, it could also give insights into populations worth targeting with countermessages. These factors, combined with the speed at which information spreads online, create ideal conditions for disinformation campaigns. They assaulted police officers, killing one and injuring others. This question format directly replicated that used in Pew Research Centre surveys dealing with disinformation [e.g. You have every right to be insulted when you read fake news, because you are in essence being treated like an idiot. Familiarity with the material operationalised here as the higher self-reported likelihood of having seen it before increases willingness to share it. House of Commons Digital, Culture, Media and Sport Committee. However, the effect sizes found were low, and effects were inconsistent across platforms and populations. It is part of a much wider information ecosystem, that in fact extends beyond hostile information operations or computational propaganda. Its intended effects include political influence, increasing group polarisation, reducing trust, and generally undermining civil society [4]. It was developed and validated using a US sample. The author thanks Christina Apelseth for her research assistance. As well as their likelihood of sharing, people were asked about the extent to which they thought the stories were likely to be true, and how likely they thought it was that they had seen them before. Intent of the main analysis, Study 2 replicates a number of findings. Similarities but also some differences from Studies 13 literacy in a sample respondents., lower Conscientiousness and male are relevant to sharing disinformation in this sample compared to the Study 1 except! Questions will help inform the development of effective interventions platforms such as Facebook and.! 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