Neutral bots probe political bias on social media

Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.

Factual corrections: Concerns and current evidence

Factual corrections that target misinformation improve belief accuracy. They do so across a wide variety of countries, political beliefs and demographic characteristics. Instances of backfire, wherein exposure to corrections reduce accuracy, are exceedingly rare and may be an artifact of research design. The evidence regarding other common concerns is mixed. While the effects on corrections on belief are not permanent, they are not entirely ephemeral, either. With some exceptions, corrections mostly only affect belief accuracy, with minor to nonexistent influence on downstream attitudes and behaviors. While corrections are not unpopular among the public, limited available evidence suggests that those who see misinformation are exceedingly unlikely to see relevant corrections.

COVID-19 vaccine equity and health equity conversations on Twitter

This study used social network analysis and trending hashtags on Twitter to identify trends related to health and vaccine equity during the Omicron wave. The analysis was conducted using consumer-friendly platforms/tools such as the Healthcare Hashtag Project and NodeXL. The study found that during the Omicron wave, there was a higher volume of tweets related to the more specific hashtag #VaccineEquity, as compared to the more general topic of #HealthEquity. The study also identified the top influencers for these hashtags and how they changed over time. The study proposes a combination of existing tools and approaches, including ontological surveillance and social network analysis, to develop proactive strategies that respond to public opinion in a timely manner. Social network analysis tools could also be useful for healthcare organizations and providers in training their staff involved in social media management to develop better social media communication strategies.

Asymmetric ideological segregation in exposure to political news on Facebook

Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increases as we shift from potential exposure to actual exposure to engagement; (ii) there is an asymmetry between conservative and liberal audiences, with a substantial corner of the news ecosystem consumed exclusively by conservatives; and (iii) most misinformation, as identified by Meta's Third-Party Fact-Checking Program, exists within this homogeneously conservative corner, which has no equivalent on the liberal side. Sources favored by conservative audiences were more prevalent on Facebook's news ecosystem than those favored by liberals.

Users choose to engage with more partisan news than they are exposed to on Google Search

If popular online platforms systematically expose their users to partisan and unreliable news, they could potentially contribute to societal issues such as rising political polarization^1,2. This concern is central to the 'echo chamber'^3-5 and 'filter bubble'^6,7 debates, which critique the roles that user choice and algorithmic curation play in guiding users to different online information sources^8-10. These roles can be measured as exposure, defined as the URLs shown to users by online platforms, and engagement, defined as the URLs selected by users. However, owing to the challenges of obtaining ecologically valid exposure data-what real users were shown during their typical platform use-research in this vein typically relies on engagement data^4,8,11-16 or estimates of hypothetical exposure^17-23. Studies involving ecological exposure have therefore been rare, and largely limited to social media platforms^7,24, leaving open questions about web search engines. To address these gaps, we conducted a two-wave study pairing surveys with ecologically valid measures of both exposure and engagement on Google Search during the 2018 and 2020 US elections. In both waves, we found more identity-congruent and unreliable news sources in participants' engagement choices, both within Google Search and overall, than they were exposed to in their Google Search results. These results indicate that exposure to and engagement with partisan or unreliable news on Google Search are driven not primarily by algorithmic curation but by users' own choices.

Information cocoons in online navigation

Social media and online navigation bring us enjoyable experiences in accessing information, and simultaneously create information cocoons (ICs) in which we are unconsciously trapped with limited and biased information. We provide a formal definition of IC in the scenario of online navigation. Subsequently, by analyzing real recommendation networks extracted from Science, PNAS, and Amazon websites, and testing mainstream algorithms in disparate recommender systems, we demonstrate that similarity-based recommendation techniques result in ICs, which suppress the system navigability by hundreds of times. We further propose a flexible recommendation strategy that addresses the IC-induced problem and improves retrieval accuracy in navigation, which are demonstrated by simulations on real data and online experiments on the largest video website in China. This paper quantifies the challenge of ICs in recommender systems and presents a viable solution, which offer insights into the industrial design of algorithms, future scientific studies, as well as policy making.

The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing

Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analysing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of 'creators', and a majority playing the role of 'consumers'. The relative proportion of these groups (approx. 14% creators-86% consumers) appears stable over time: consumers are mostly exposed to the opinions of a vocal minority of creators (which are the origin of 82% of fake content in our data), that could be mistakenly understood as representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic.

Most users do not follow political elites on Twitter; those who do show overwhelming preferences for ideological congruity

We offer comprehensive evidence of preferences for ideological congruity when people engage with politicians, pundits, and news organizations on social media. Using 4 years of data (2016-2019) from a random sample of 1.5 million Twitter users, we examine three behaviors studied separately to date: (i) following of in-group versus out-group elites, (ii) sharing in-group versus out-group information (retweeting), and (iii) commenting on the shared information (quote tweeting). We find that the majority of users (60%) do not follow any political elites. Those who do follow in-group elite accounts at much higher rates than out-group accounts (90 versus 10%), share information from in-group elites 13 times more frequently than from out-group elites, and often add negative comments to the shared out-group information. Conservatives are twice as likely as liberals to share in-group versus out-group content. These patterns are robust, emerge across issues and political elites, and exist regardless of users' ideological extremity.

Partisan asymmetries in exposure to misinformation

Online misinformation is believed to have contributed to vaccine hesitancy during the Covid-19 pandemic, highlighting concerns about social media’s destabilizing role in public life. Previous research identified a link between political conservatism and sharing misinformation; however, it is not clear how partisanship affects how much misinformation people see online. As a result, we do not know whether partisanship drives exposure to misinformation or people selectively share misinformation despite being exposed to factual content. To address this question, we study Twitter discussions about the Covid-19 pandemic, classifying users along the political and factual spectrum based on the information sources they share. In addition, we quantify exposure through retweet interactions. We uncover partisan asymmetries in the exposure to misinformation: conservatives are more likely to see and share misinformation, and while users’ connections expose them to ideologically congruent content, the interactions between political and factual dimensions create conditions for the highly polarized users—hardline conservatives and liberals—to amplify misinformation. Overall, however, misinformation receives less attention than factual content and political moderates, the bulk of users in our sample, help filter out misinformation. Identifying the extent of polarization and how political ideology exacerbates misinformation can help public health experts and policy makers improve their messaging.

Botometer 101: social bot practicum for computational social scientists

Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore, it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.

How Twitter data sampling biases U.S. voter behavior characterizations

Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.

A general framework to link theory and empirics in opinion formation models

We introduce a minimal opinion formation model that is quite flexible and can reproduce a wide variety of the existing micro-influence assumptions and models. The model can be easily calibrated on real data, upon which it imposes only a few requirements. From this perspective, our model can be considered as a bridge, connecting theoretical studies on opinion formation models and empirical research on social dynamics. We investigate the model analytically by using mean-field approximation and numerically via Monte Carlo simulations. Our analysis is exemplified by recently reported empirical data drawn from an online social network. We demonstrate that the model calibrated on these data may reproduce fragmented and polarizing social systems. Furthermore, we manage to generate an artificial society that features properties quantitatively and qualitatively similar to those observed empirically at the macro scale. This ability became possible after we had advanced the model with two important communication features: selectivity and personalization algorithms.

Political audience diversity and news reliability in algorithmic ranking

Newsfeed algorithms frequently amplify misinformation and other low-quality content. How can social media platforms more effectively promote reliable information? Existing approaches are difficult to scale and vulnerable to manipulation. In this paper, we propose using the political diversity of a website's audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 US residents, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards. We then incorporate audience diversity into a standard collaborative filtering framework and show that our improved algorithm increases the trustworthiness of websites suggested to users-especially those who most frequently consume misinformation-while keeping recommendations relevant. These findings suggest that partisan audience diversity is a valuable signal of higher journalistic standards that should be incorporated into algorithmic ranking decisions.