Written evidence submitted by Andreu Casas, Georgia Dagher, and Ben O’Loughlin (SMH0030)
Social Media, Misinformation and Harmful Algorithms: What We Know, and Don’t Know, about Political Online Safety.
New Political Communication Unit
Department of Politics, International Relations and Philosophy
Royal Holloway University of London
Prepared for the Science, Innovation and Technology Committee, House of Commons, UK Parliament
Social media plays a key role in politics, as people increasingly use it to consume news and learn about and engage with politics. Social media can contribute to healthier societies by connecting people with diverse viewpoints and giving a voice to the voiceless. However, it can also cause harm. Some emphasize the perils of social media for the mental health of young adults, and of AI-powered dis/misinformation for the health of our democracies.
The goal of this report is three-fold. First, we list a set of threats to our political wellbeing that emerge from social media platforms. Second, we provide an overview of existing literature on each threat (what we know). Finally, we discuss key pressing questions that future research should address (what we don’t know, yet).
We, the New Political Communication Unit at Royal Holloway, assembled the information here as part of a report on Academic Access to Social Media Data for the study of “Political Online Safety” – to be submitted to an Ofcom consultation on “Researchers’ access to information from regulated online services”.
Political online safety refers to protecting people’s political attitudes and behaviour, and political institutions and processes more generally, from online threats such misinformation, hateful content, and foreign influence operations.
Definition. Misinformation refers to the dissemination of false information that can mislead the public (Jackson 2017; Sanders and Jones 2018; United Kingdom Digital, Culture, Media and Sport Committee 2019), and disinformation is false information that is propagated deliberately (Tucker et al. 2018).
Prevalence. Research shows that although only a small number of social media users are responsible for the spread of misinformation (Grinberg et al. 2019; Guess et al. 2019), misinformation spreads faster than other kinds of content (Vosoughi et al. 2018), and many users report having been exposed to some kind of misinformation (Lee et al. 2023). In the United Kingdom, Chadwick et al. (2018) found 9% of Twitter users to deliberately share disinformation, and another survey found 15% of respondents to have also deliberately engaged in the dissemination of false information (Deltapoll 2021). In a recent survey, about 40% of adults in the United Kingdom reported having encountered misinformation online (Ofcom 2024).
Predictors. More ideologically extreme, particularly conservative and older individuals, are more likely to be exposed to and share misinformation (Grinberg et al. 2019; Guess et al. 2019). Other predictors include gender (male), low media literacy, prior exposure to misinformation, distrust in the media, and a need for entertainment and socialisation (Sun and Xie 2024).
Effects. Exposure to misinformation leads to lower trust in the media and democratic institutions, and increased polarisation (Ognyanova et al. 2020; Azzimonti and Fernandes 2023).
Research Gaps. Future research should look into the generalisability of findings, which are mostly based data from the United States, including asymmetries in believing and spreading false information (on the United States, see Guess et al. 2019; González-Bailón et al. 2023), determinants of public support for combating online misinformation, and the role of governments, social media platforms, and users in this process (Jang et al. 2023), and the effectiveness (and unintentional effects) of fact-checking and content-moderation interventions (Pennycook et al. 2018; Tan 2022; Allen et al. 2024). Additionally, with the emergence of generative artificial intelligence, future research will need to adapt to studying misinformation that is increasingly realistic and sophisticated.
Definition. Toxicity online includes incivility, intolerance, and violent threats. In its most extreme form, it can be characterised as hate speech, which refers to acts that advocate, incite, or justify discrimination and violence against a specific group (United Nations Strategy and Plan of Action on Hate Speech 2019; Hietanen and Eddebo 2023).
Prevalence. Politicians are often the target of toxic speech. Between March and April 2022, politicians in the United Kingdom received more than 3,000 offensive tweets a day (Lynch et al. 2022). There seems to be an upper trend: 4.5% of the replies to candidates on Twitter during the 2019 election contained toxic language, compared to 3.3% in 2017 (Gorell et al. 2020). Male candidates receive more political abuse in general, while women candidates receive more sexist comments, and ethnic minority MPs receive more racist ones (Gorell et al. 2020). In the United States,18% of tweets mentioning members of Congress in 2017-2018 contained uncivil language (Theocharis et al. 2020) and 1% of tweets mentioning Trump and Clinton in 2016 contained extreme hate speech (Siegel et al. 2021). Regular users, not only politicians, are also the target of toxic speech. A 2021 survey reveals that 20% of American adults have experienced online harassment (Vogels 2021).
Predictors. Platform-level predictors for the spread of online toxicity include platform affordances, such as the level of user anonymity (Barlett, 2015; Zimmerman and Ybarra 2016; Moore et al. 2021). User-level predictors include higher tolerance for negativity and sensitivity to toxic content (Pradel et al. 2024; Pradel and Theocharis 2024), higher levels of polarisation (Saveski et al. 2021), and prior exposure to toxic comments (Kim et al. 2021).
Effects. Over time, online hate and toxicity can incite real-world violence such as terrorist attacks, hate crimes, and harassment (see Williams et al. 2020), and may contribute to silencing targeted groups and lead them to exit certain platforms (Nadim and Fladmoe 2021; Pradel and Theocharis 2024). Online toxicity directed at women politicians can negatively affect their decisions to (re)run for elections (Gorell et al. 2020), perpetuating gender inequalities in politics.
Research Gaps. Future research should examine the nuances of toxic behaviour, as this comes in different formats, such as text, images, and cartoons (presented as entertainment; see Regehr et al. 2024). Additionally, we need further cross-platform research for a full picture of how toxic speech evolves and spreads across the social media ecosystem, and for a better understanding of the correlates between toxic behaviour and platform affordances (Munn 2020) and the extent to which platform policies or algorithms, versus human behaviour, are to blame for the spread of toxicity online (Munger and Philips 2019; Ledwich and Zaitsev 2020; Hokka 2021).
Definition. Echo chambers refer to people following accounts and content that reflect their own political views (Barberá 2020), and filter bubbles refer to the role of platform algorithms in sorting users into ideologically congruent networks (Barberá 2020). Ideological polarisation refers to people holding increasingly divergent views, and affective polarisation refers to people holding increasingly negative views towards members of a political out-group (Kubin and von Sikorski 2021).
Prevalence. In the United States most social media users, particularly the politically-interested, are embedded in networks of like-minded accounts (Barberá et al. 2015, 2020; Nyhan et al. 2023; Wojcieszak et al. 2022). Yet, echo chambers seem to be weaker in European countries (Vaccari and Valeriani 2021). Additionally, cross-cutting interactions are not as rare as some public commentators argue (Sunstein 2017). Barberá et al. (2015) find that for about 75% of Twitter users from Germany, Spain, and the United States, at least 25% of their following are accounts with a different ideology. Eady et al. (2019) find a substantial overlap between the media accounts followed by liberals and conservatives on social media in the United States. Regarding polarisation, despite numerous evidence pointing to increased ideological and affective polarisation in the United States (Iyengar et al. 2019), these patterns do not always apply to other Western democracies (Boxell et al. 2024). In the United Kingdom, ideological polarisation has remained mostly stable (Boxell et al. 2024) with a few exceptions, such as increasingly divergent views on immigration (Tipoe and Lee 2024). Affective polarisation, however, is slightly on the rise (Garzia et al. 2023), with group identities and public debates around Brexit playing a key role in this trend (Hobolt et al. 2020). Finally, polarising content such as anti-immigration posts, although mostly generated by a small number of users, travels faster than neutral content, in part thanks to these accounts being embedded in like-minded networks (Nasuto and Rowe 2024).
Predictors. Echo chambers are mostly the result of users deciding to follow like-minded accounts and content. Research points to age (older people) and ideology (liberals and conservatives, v. independents and moderates) as key predictors of slanted online media diets (Guess 2021). Yet, platform algorithms exacerbate these further (Barberá 2020). For example, Bashky et al. (2015) and Gonzalez-Bailón et al. (2023) show that, in two different time periods, Facebook algorithmic ranking reduced exposure to cross-cutting content by about 15%. There are mix-findings regarding whether online echo chambers are predictive of increased polarisation. Boxell et al. (2017) find higher levels of polarisation among older generations, who are the least likely to use social media; and in a deactivation experiment on Facebook, Nyhan et al. (2023) show no clear relationship between exposure to like-minded news sources and ideological/affective polarisation.
Effects. Ideological polarisation can lead to legislative gridlock (Jones 2001) and increased dissatisfaction with democracy (Wagner 2001; Torcal and Magalhães 2022). Affective polarisation can lead to a more toxic online environment (Saveski et al. 2021) especially over time (Nelimarkka et al. 2018). Additionally, affective polarisation can lead to increased support for political violence (Kalmoe and Mason 2022), although other work argues the relationship has been overstated (Westwood et al. 2022).
Research Gaps. Future research should investigate further the role of platform algorithms, versus users, in driving polarisation – particularly given the rise of platforms such as TikTok, where recommendations play a much more crucial role in the curation of content. Additionally, further research is needed to understand how exposure to cross-cutting information online, or the lack thereof, shapes political attitudes and behaviour.
Definition. Extremism refers to holding extreme political views and radicalisation to the process of one’s views becoming more extreme.
Prevalence. Extreme views on major social media platforms are rare. Barberá et al. (2015) and Wojcieszak et al. (2022) show that the ideological distribution of ordinary users on X is fairly moderate, following a normal distribution. Bond and Messing (2015) and Eady et al. (2024) find ordinary users to follow a bimodal ideological distribution on Facebook and X, respectively, yet they also find extreme ideologists to be rare. In a dataset of tweets mentioning Trump and Clinton during the 2016 United States election, Siegel et al. (2021) find white supremacist language in less than 0.02% of tweets. In a study of convicted terrorists in the United Kingdom, Gill et al. (2017) find them to be significantly more likely to learn and communicate online. Finally, extreme views seem more prevalent in particular niche platforms, such as Gab (Zennettou et al. 2018), Tumblr (Nagle 2017), Parler (Stevenson et al. 2023), Rumble, and Odysee (as well as Telegram groups, see Al-Rawi 2021).
Predictors. Key predictors mainly point to platform affordances, such as increased anonymity (Awan et al. 2019), softer content moderation policies (Zennettou et al. 2018; Colley and Moore 2022), and algorithmic biases that push users into “rabbit holes” (Tufekci 2018; Barnes 2022; Brown et al. 2022) – although the latter has been overstated (Brown et al. 2022). Research finds that users with extreme views are more active and their content spreads faster (Siegel et al. 2021; Wojcieszak et al. 2022; Eady et al. 2024).
Effects. Exposure to extreme views can lead to ideological radicalisation (Koehler 2014; Magdy et al. 2016). Research points to younger citizens and first-time voters being particularly susceptible to radicalisation, and to extreme content being more effective when users can relate to the content and messenger (Karl 2017). Online radicalisation can lead to violence offline. Pauwels and Schils (2016) find higher self-reported political violence among young citizens that consume extreme content online.
Research Gaps. Most research focuses on major social media platforms, while extreme and radicalising content often originates in smaller platforms. Future research should look into the wider social media ecosystem (Bovet and Grindrod 2022), paying closer attention to how content travels between niche and major platforms (Buntain et al. 2021). Further research should also look into the effectiveness of platform moderation policies: are there cross-platform differences in the moderation of extreme and radicalising content? What interventions are most effective at reducing extreme views? Do extreme users radicalise further when expelled from mainstream platforms, by being pushed into niche platforms with increased exposure to extreme content?
Definition. Artificial intelligence (AI) refers to automatic systems trained to perform a given task. AI bias refers to these automatic systems systematically underperforming in a way that results in unfair outcomes for particular groups of users (Ferrara 2023).
Prevalence. Regarding content moderation, research identifies several AI tools as having a racial dialect bias (Davidson et al. 2019; Sap et al. 2019; Ball-Burack et al. 2021) and to silence and censor members of marginalised communities, even when they abide by platforms’ rules (Haimson and Hoffman 2016; Cook 2019; Joseph 2019; Van Horne 2019; Are 2020; Haimson et al. 2021). AI tools designed to identify duplicates may be insensitive to the use of the same content in a different context (terrorist propaganda being reposted in a journalistic context; Llansó 2019). Regarding recommendation algorithms, research finds platform algorithms to boost, at least to some extent, extreme content, misinformation, hate speech, radical ideologies, and conspiracy theories (Sunstein 2017; Tufekci 2018; Munn 2020; Ahmed and Bales 2021; Barnes 2022; Brown et al. 2022; Wang et al. 2022; Nasuto and Rowe 2024; Regehr et al. 2024; Wischerath et al. 2024). However, other research finds platform algorithms to actually reduce exposure to untrustworthy content (Guess et al. 2023).
Determinants. Content from minorities and people with non-Western backgrounds is more likely to be unfairly moderated by AI systems (Haimson and Hoffman 2016; Cook 2019; Davidson et al. 2019; Joseph 2019; Sap et al. 2019; Van Horne 2019; Are 2020; Ball-Burack et al. 2021; Haimson et al. 2021; Casas 2024). Toxic content is sometimes recommended at higher rates (Sunstein 2017; Tufekci 2018; Munn 2020; Ahmed and Bales 2021; Barnes 2022; Brown et al. 2022; Wang et al. 2022; Nasuto and Rowe 2024; Regehr et al. 2024; Wischerath et al. 2024).
Effects. Unfair moderation of content from minorities and users of non-Western culture/origin can lead to biased political conversations online (Van Horne 2019; Casas 2024; Webb-Williams et al. 2024). Biases in the recommendation of extreme views, misinformation, and harmful content at higher rates can lead to an increased toxicity, radicalisation, and violence (Magdy et al. 2016; Pauwels and Schills 2016).
Research Gaps. There is a need for more audit-type research looking into potential biases in the moderation (Casas 2024; Mosleh et al. 2024) and recommendation of content (Brown et al. 2022), and other tasks – particularly given the rise of generative AI. We also need more research developing and testing interventions for unbiasing biased systems, and assessing how AI biases shape politically relevant conversations.
Definition. Information operations, or influence operations, refer to when states engage in the collection/dissemination of information online to advance their (geopolitical) interests, by criticising an adversary and/or promoting their own narratives (Miskimmon et al. 2013; Golovchenko et al. 2020; Bergh 2024). Election interference is a type of information operation where the aim is to influence the outcome of an election, either by promoting/demoting a given candidate/party, polarising the electorate, or suppressing participation (Golovchenko et al. 2020; Bradshaw et al. 2021).
Predictors. Predictors of being the target of different kinds of influence operations vary by context, and include demographic characteristics and partisanship. In the 2016 United States election, messages from the Russian Internet Research Agency (IRA) that targeted Republicans emphasised immigration, race, and ethnicity; and messages that targeted African Americans emphasised structural inequalities, and fomented lower turnout (Howard et al. 2018; Freelon et al. 2020).
Effects. Research suggests that the operations conducted by the IRA did not significantly impact the outcome of the elections mentioned above. Exposure to influence operations tends to be heavily concentrated on specific groups of voters (Eady et al. 2023), to be less prevalent than content from domestic and trustworthy sources (Tucker 2020; Eady et al. 2023), and the number of accounts linked to these operations is generally small (Booth et al. 2017). Many governments are increasingly implementing prevention measures (Brattberg and Maurer 2018; Bateman and Jackson 2024). However, it remains difficult to assess the long-term impact of these operations, and they may have indirect effects, such as the spread of conspiracy theories following the votes, particularly if domestic authentic networks become involved – as shown by the attack on the United States Capitol in 2021.
Research Gaps. Existing research is often limited to single cases/elections (Francois and Douek 2021; Eady et al. 2023). Further research should investigate the longer-term, and cross-country, patterns and effects (Martin et al. 2020, 2023). Platforms have made great efforts to fight influence operations (for example, the Disinfodex database) but assessing the impact of takedowns is difficult, as actors may adapt and change their behaviour over time. Takedowns are only (a small) part of the solution to combating influence operations. Further research must also examine the role of recommendation algorithms in spreading election-related misinformation; plus the effectiveness of a wider range of interventions.
Definition. Political micro-targeting refers to the collection of online behavioural data from individuals in order to deliver them tailored political advertising (Dobber et al. 2019).
Prevalence. In a study of political ads Facebook and Instagram ads across 95 countries and 113 elections, Votta et al. (2024) show that political ads on social media platforms are now common across the globe. Most ads target users based on a single or two criteria. Geographic location and socio-demographic characteristics are the most common targeting criteria, followed by interests and behaviour online (Votta el al. 2024). In the 2024 United Kingdom General Election, political parties spent more than 2.5 million pounds on Google Ads, and an average of about 1 million pounds a week on Meta ads (Plevin 2024).
Predictors. Online micro-targeting is more present in Western democracies and wealthier countries (Votta et al. 2024). Democratic countries with a proportional (v. majoritarian) electoral system, with limits on traditional media campaign spending, and with stricter data protection laws, are more likely to run targeted social media ads (Votta et al. 2024). Right-leaning parties are more likely to target older men, and left-leaning parties are more likely to target younger and female voters (Votta et al. 2024).
Effects. In a study of the 2021 Dutch election, Chu et al. (2024) found a sample of 505 participants to be exposed to 9,000 ads from political parties on Facebook. The ads had an effect on propensity to vote and voting choice. In a meta-analysis of campaign experiments conducted by Democrats in the 2018 and 2020 United States elections, Hewitt et al. (2024) find a small but meaningful variation in the persuasive effects of political online ads. Tappin et al. (2024) find micro-targeting to be most effective at shifting policy views when based on a single (v. many) individual characteristic.
Research Gaps. The vast majority of research on political micro-targeting is based on a few countries (such as the United States, Netherlands). Future research should explore the prevalence, determinants, and effects of micro-targeting in other countries. Additionally, we must learn about the prevalence of micro-targeted campaigns across platforms, from which party/ideology, and the relative ability of online ads to persuade voters based on different targeted characteristics.
18 December 2024
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