The misinformation machine

The benefits and dangers of anthropomorphic conversational agents

A growing body of research suggests that the recent generation of large language model (LLMs) excel, and in many cases outpace humans, at writing persuasively and empathetically, at inferring user traits from text, and at mimicking human-like conversation believably and effectively-without possessing any true empathy or social understanding. We refer to these systems as "anthropomorphic conversational agents" to aptly conceptualize the ability of LLM-based systems to mimic human communication so convincingly that they become increasingly indistinguishable from human interlocutors. This ability challenges the many efforts that caution against "anthropomorphizing" LLMs, attaching human-like qualities to nonhuman entities. When the systems themselves exhibit human-like qualities, calls to resist anthropomorphism will increasingly fall flat. While the AI industry directs much effort into improving the reasoning abilities of LLMs-with mixed results-the progress in communicative abilities remains underappreciated. In this perspective, we aim to raise awareness for both the benefits and dangers of anthropomorphic agents. We ask: should we lean into the human-like abilities, or should we aim to dehumanize LLM-based systems, given concerns over anthropomorphic seduction? When users cannot tell the difference between human interlocutors and AI systems, threats emerge of deception, manipulation, and disinformation at scale. We suggest that we must engage with anthropomorphic agents across design and development, deployment and use, and regulation and policy-making. We outline in detail implications and associated research questions.

News and misinformation consumption: A temporal comparison across European countries

The Internet and social media have transformed the information landscape, democratizing content access and production. While making information easily accessible, these platforms can also act as channels for spreading misinformation, posing crucial societal challenges. To address this, understanding news consumption patterns and unraveling the complexities of the online information environment are essential. Previous studies highlight polarization and misinformation in online discussions, but many focus on specific topics or contexts, often overlooking comprehensive cross-country and cross-topic analyses. However, the dynamics of debates, misinformation prevalence, and the efficacy of countermeasures are intrinsically tied to socio-cultural contexts. This work aims to bridge this gap by exploring information consumption patterns across four European countries over three years. Analyzing the Twitter activity of news outlets in France, Germany, Italy, and the UK, this study seeks to shed light on how topics of European significance resonate across these nations and the role played by misinformation sources. The results spotlight that while reliable sources predominantly shape the information landscape, unreliable content persists across all countries and topics. Though most users favor trustworthy sources, a small percentage predominantly consumes content from questionable sources, with even fewer maintaining a mixed information diet. The cross-country comparison unravels disparities in audience overlap among news sources, the prevalence of misinformation, and the proportion of users relying on questionable sources. Such distinctions surface not only across countries but also within various topics. These insights underscore the pressing need for tailored studies, crucial in designing targeted and effective countermeasures against misinformation and extreme polarization in the digital space.

Effect of nutrition-related infodemics and social media on maternal experience: A nationwide survey in a low/middle income country

BACKGROUND

Undernutrition is a crucial cause of morbidity and mortality among children in low- or middle-income countries (LMICs). A better understanding of maternal general healthy nutrition knowledge, as well as misbeliefs, is highly essential, especially in such settings. In the current era of infodemics, it is very strenuous for mothers to select not only the right source for maternal nutrition information but the correct information as well.

AIM

To assess maternal healthy nutritional knowledge and nutrition-related misbeliefs and misinformation in an LMIC, and to determine the sources of such information and their assessment methods.

METHODS

This cross-sectional analytical observational study enrolled 5148 randomly selected Egyptian mothers who had one or more children less than 15 years old. The data were collected through online questionnaire forms: One was for the general nutrition knowledge assessment, and the other was for the nutritional myth score. Sources of information and ways of evaluating internet sources using the Currency, Relevance, Authority, Accuracy, and Purpose test were additionally analyzed.

RESULTS

The mean general nutrition knowledge score was 29 ± 9, with a percent score of 70.8% ± 12.1% (total score: 41). The median myth score was 9 (interquartile range: 6, 12; total score: 18). The primary sources of nutrition knowledge for the enrolled mothers were social media platforms (55%). Half of the mothers managed information for currency and authority, except for considering the author's contact information. More than 60% regularly checked information for accuracy and purpose. The mothers with significant nutrition knowledge checked periodically for the author's contact information (P = 0.012). The nutrition myth score was significantly lower among mothers who periodically checked the evidence of the information (P = 0.016). Mothers dependent on their healthcare providers as the primary source of their general nutritional knowledge were less likely to hold myths by 13% (P = 0.044). However, using social media increased the likelihood of having myths among mothers by approximately 1.2 (P = 0.001).

CONCLUSION

Social media platforms were found to be the primary source of maternal nutrition information in the current era of infodemics. However, healthcare providers were the only source for decreasing the incidence of maternal myths among the surveyed mothers.

Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections

Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter's news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers-users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.

MisRoBÆRTa: Transformers versus Misinformation

Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while lacking the rigor of traditional journalism. Transformers and transfer learning proved to be state-of-the-art methods for multiple well-known natural language processing tasks. In this paper, we propose MisRoBÆRTa, a novel transformer-based deep neural ensemble architecture for misinformation detection. MisRoBÆRTa takes advantage of two state-of-the art transformers, i.e., BART and RoBERTa, to improve the performance of discriminating between real news and different types of fake news. We also benchmarked and evaluated the performances of multiple transformers on the task of misinformation detection. For training and testing, we used a large real-world news articles dataset (i.e., 100,000 records) labeled with 10 classes, thus addressing two shortcomings in the current research: (1) increasing the size of the dataset from small to large, and (2) moving the focus of fake news detection from binary classification to multi-class classification. For this dataset, we manually verified the content of the news articles to ensure that they were correctly labeled. The experimental results show that the accuracy of transformers on the misinformation detection problem was significantly influenced by the method employed to learn the context, dataset size, and vocabulary dimension. We observe empirically that the best accuracy performance among the classification models that use only one transformer is obtained by BART, while DistilRoBERTa obtains the best accuracy in the least amount of time required for fine-tuning and training. However, the proposed MisRoBÆRTa outperforms the other transformer models in the task of misinformation detection. To arrive at this conclusion, we performed ample ablation and sensitivity testing with MisRoBÆRTa on two datasets.

Infodemics: A new challenge for public health

The COVID-19 information epidemic, or "infodemic," demonstrates how unlimited access to information may confuse and influence behaviors during a health emergency. However, the study of infodemics is relatively new, and little is known about their relationship with epidemics management. Here, we discuss unresolved issues and propose research directions to enhance preparedness for future health crises.

Conspiracy vs science: A large-scale analysis of online discussion cascades

With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments from an online social media platform, we investigate the propagation of two distinct narratives-(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable. We find that conspiracy cascades tend to propagate in a multigenerational branching process whereas science cascades are more likely to grow in a breadth-first manner. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, and is more viral and bursty than science information. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. We also find that conspiracy cascades are much more concerned with political and controversial topics. After applying machine learning models, we achieve an AUC score of nearly 90% in discriminating conspiracy from science narratives using the constructed features. We further investigate user's role during the growth of cascades. In contrast with previous assumption that misinformation is primarily driven by a small set of users, we find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades, imposing new challenges on the management of misinformation. Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information; Instead, we find that conspiracy information from media outlets with left or right orientation triggers smaller cascades and is less viral than information from online social media platforms (e.g., Twitter and Imgur) whose political orientations are unclear. Our study provides complementing evidence to current misinformation research and has practical policy implications to stem the propagation and mitigate the influence of misinformation online.

On fake news, gatekeepers and LIS professionals: the finger or the moon?

Purpose

The purpose of this paper is to reflect on the meaning of fake news in the digital age and on the debate on disinformation in scholarly literature, in the light of the ethics of library and information profession.

Design/methodology/approach

Revision of a keynote address at the BOCATSSS2020 conference, this paper offers an overview of current literature comparing it with a moment in the past that was crucial for information: post-Second World War time, when Wiener (1948) founded cybernetics and C.P. Snow advocated for “The two cultures” (1959).

Findings

The complex issue demands a multi-disciplinary approach: there is not one solution, and some approaches risk limiting the freedom of expression, yet countering the phenomenon is a moral obligation for library and information science professionals.

Originality/value

Comparing the present digital revolution with the past, this paper opens questions on the ethical commitment of information professionals.

The COVID-19 social media infodemic

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

The limited reach of fake news on Twitter during 2019 European elections

The advent of social media changed the way we consume content, favoring a disintermediated access to, and production of information. This scenario has been matter of critical discussion about its impact on society, magnified in the case of the Arab Springs or heavily criticized during Brexit and the 2016 U.S. elections. In this work we explore information consumption on Twitter during the 2019 European Parliament electoral campaign by analyzing the interaction patterns of official news outlets, disinformation outlets, politicians, people from the showbiz and many others. We extensively explore interactions among different classes of accounts in the months preceding the elections, held between 23rd and 26th of May, 2019. We collected almost 400,000 tweets posted by 863 accounts having different roles in the public society. Through a thorough quantitative analysis we investigate the information flow among them, also exploiting geolocalized information. Accounts show the tendency to confine their interaction within the same class and the debate rarely crosses national borders. Moreover, we do not find evidence of an organized network of accounts aimed at spreading disinformation. Instead, disinformation outlets are largely ignored by the other actors and hence play a peripheral role in online political discussions.