A 61-million-person experiment in social influence and political mobilization

To participate or not to participate? Influence mechanism of artificial intelligence on Chinese college students' willingness to participate in online politics

This study probes the mechanism of artificial intelligence's (AI's) influence on Chinese college students' willingness to participate in online politics and constructs a theoretical model based on the theory of planned behavior. Through the analysis of questionnaire data acquired from up to 317 Chinese college students in total, it turns out that the use of AI affects Chinese college students' willingness to the participation of online political practice significantly and positively, and such online political participation cognition of Chinese college students plays a mediating role, three aspects of which included as the followings on behavioral attitudes, subjective norms, and perceived behavioral control. Additionly, media literacy level plays a moderating role in online political participation cognition and willingness to participate. All the findings highlight the importance of optimizing the online political participation environment, enhancing college students' cognition of political participation, and improving media literacy in the context of the digital era, which provides practical guidance for promoting healthy and positive online political participation among college students.

Long-term tracking of social structure in groups of rats

Rodents serve as an important model for examining both individual and collective behavior. Dominance within rodent social structures can determine access to critical resources, such as food and mating opportunities. Yet, many aspects of the intricate interplay between individual behaviors and the resulting group social hierarchy, especially its evolution over time, remain unexplored. In this study, we utilized an automated tracking system that continuously monitored groups of male rats for over 250 days to enable an in-depth analysis of individual behavior and the overarching group dynamic. We describe the evolution of social structures within a group and additionally investigate how past behaviors influence the emergence of new social hierarchies when group composition and experimental area changes. Notably, we find that conventional individual and pairwise tests exhibit a weak correlation with group behavior, highlighting their limited accuracy in predicting behavioral outcomes in a collective context. These results emphasize the context-dependence of social behavior as an emergent property of interactions within a group and highlight the need to measure and quantify social behavior in more naturalistic environments.

TrustGNN: Graph Neural Network-Based Trust Evaluation via Learnable Propagative and Composable Nature

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Challenges in Understanding Human-Algorithm Entanglement During Online Information Consumption

Most content consumed online is curated by proprietary algorithms deployed by social media platforms and search engines. In this article, we explore the interplay between these algorithms and human agency. Specifically, we consider the extent of entanglement or coupling between humans and algorithms along a continuum from implicit to explicit demand. We emphasize that the interactions people have with algorithms not only shape users' experiences in that moment but because of the mutually shaping nature of such systems can also have longer-term effects through modifications of the underlying social-network structure. Understanding these mutually shaping systems is challenging given that researchers presently lack access to relevant platform data. We argue that increased transparency, more data sharing, and greater protections for external researchers examining the algorithms are required to help researchers better understand the entanglement between humans and algorithms. This better understanding is essential to support the development of algorithms with greater benefits and fewer risks to the public.

Social impediments to meat-eaters' adherence to critical calls for a meat-free diet: An experimental test of social norm and message source effects

Reducing meat consumption is highly effective for reducing personal carbon emissions, yet most people in Western nations still eat meat. We build on recent research highlighting that group boundaries may impede dietary change by (a) promoting pro-meat norms and (b) prohibiting critical calls for a veg* diet (vegetarian and vegan, i.e., meat-free). Past research relied on self-reports and behavioural measures of engagement, leaving open whether these effects extend to food consumption settings and ad-hoc meal choice. We conducted two pre-registered experiments in which meat-eaters read critical calls to adopt a veg* diet, either by a vegan (outgroup) or a meat-eater (ingroup). In Experiment 2, participants moreover read an article either highlighting a veg* or a meat-eating norm. We then assessed actual (Experiment 1) or hypothetical (Experiment 2) meal choice as dependent variables. As predicted, intergroup criticism (i.e., voiced by veg*s) consistently led to message rejection in comparison to the same criticism voiced by meat eaters, but we did not observe effects on meal choice. Norms neither had a main nor interaction effect on self-reports and behaviour. We discuss potential intermediary processes between engagement with and adoption of a vegan diet and derive evidence-based recommendations for constructive communication across group boundaries.

How Social Media Algorithms Shape Offline Civic Participation: A Framework of Social-Psychological Processes

Even though social media platforms have created opportunities for more efficient and convenient civic participation, they are unlikely to bring about social change if the online actions do not propagate to offline civic participation. This article begins by reviewing the meta-analytic evidence on the relation between social media use and offline civic participation. Following this discussion, we present a theoretical framework that incorporates the attitudinal, motivational, and relational processes that may mediate the effect of social media use on offline civic participation. The framework highlights how social media algorithms may shape attitudes on important societal issues, promote generalized action goals among habitual users, and build social capital. We further discuss factors that may strengthen or undermine each of these processes, suggest ways to design and implement algorithms that may promote offline civic participation, and propose questions for future research.

Network spillover effects associated with the ChooseWell 365 workplace randomized controlled trial to promote healthy food choices

Food choices are closely linked to culture, social relationships, and health. Because many adults spend up to half their time at work, the workplace provides a venue for changing population health-related behaviors and norms. It is unknown whether the effects of a workplace intervention to improve health behaviors might spread beyond participating employees due to social influence. ChooseWell 365 was a randomized controlled trial testing a 12-month healthy eating intervention grounded in principles of behavioral economics. This intervention leveraged an existing cafeteria traffic-light labeling system (green = healthy; red = unhealthy) in a large hospital workplace and demonstrated significant improvements in healthy food choices by employees in the intervention vs. control group. The current study used data from over 29 million dyadic purchasing events during the trial to test whether social ties to a trial participant co-worker (n = 299 intervention, n = 302 control) influenced the workplace food choices of non-participants (n = 7900). There was robust evidence that non-participants who were socially tied to more intervention group participants made healthier workplace food purchases overall, and purchased a greater proportion of healthy (i.e., green) food and beverages, and fewer unhealthy (i.e., red) beverages and modest evidence that the benefit of being tied to intervention participants was greater than being tied to control participants. Although individual-level effect sizes were small, a range of consistent findings indicated that this light-touch intervention yielded spillover effects of healthy eating behaviors on non-participants. Results suggest that workplace healthy eating interventions could have population benefits extending beyond participants.

Epidemic modelling suggests that in specific circumstances masks may become more effective when fewer contacts wear them

BACKGROUND

The effectiveness of non-pharmaceutical interventions to control the spread of SARS-CoV-2 depends on many contextual factors, including adherence. Conventional wisdom holds that the effectiveness of protective behaviours, such as wearing masks, increases with the number of people who adopt them. Here we show in a simulation study that this is not always true.

METHODS

We use a parsimonious network model based on the well-established empirical facts that adherence to such interventions wanes over time and that individuals tend to align their adoption strategies with their close social ties (homophily).

RESULTS

When these assumptions are combined, a broad dynamic regime emerges in which the individual-level reduction in infection risk for those adopting protective behaviour increases as adherence to protective behaviour decreases. For instance, at 10 % coverage, we find that adopters face nearly a 30 % lower infection risk than at 60 % coverage. Based on surgical mask effectiveness estimates, the relative risk reduction for masked individuals ranges from 5 % to 15 %, or a factor of three. This small coverage effect occurs when the outbreak is over before the pathogen is able to invade small but closely knit groups of individuals who protect themselves.

CONCLUSIONS

Our results confirm that lower coverage reduces protection at the population level while contradicting the common belief that masking becomes ineffective at the individual level as more people drop their masks.

The effects of Facebook and Instagram on the 2020 election: A deactivation experiment

We study the effect of Facebook and Instagram access on political beliefs, attitudes, and behavior by randomizing a subset of 19,857 Facebook users and 15,585 Instagram users to deactivate their accounts for 6 wk before the 2020 U.S. election. We report four key findings. First, both Facebook and Instagram deactivation reduced an index of political participation (driven mainly by reduced participation online). Second, Facebook deactivation had no significant effect on an index of knowledge, but secondary analyses suggest that it reduced knowledge of general news while possibly also decreasing belief in misinformation circulating online. Third, Facebook deactivation may have reduced self-reported net votes for Trump, though this effect does not meet our preregistered significance threshold. Finally, the effects of both Facebook and Instagram deactivation on affective and issue polarization, perceived legitimacy of the election, candidate favorability, and voter turnout were all precisely estimated and close to zero.

Disentangling associations between pubertal development, healthy activity behaviors, and sex in adolescent social networks

With the onset of puberty, youth begin to choose their social environments and develop health-promoting habits, making it a vital period to study social and biological factors contextually. An important question is how pubertal development and behaviors such as physical activity and sleep may be differentially linked with youths' friendships. Cross-sectional statistical network models that account for interpersonal dependence were used to estimate associations between three measures of pubertal development and youth friendships at two large US schools drawn from the National Longitudinal Study of Adolescent to Adult Health. Whole-network models suggest that friendships are more likely between youth with similar levels of pubertal development, physical activity, and sleep. Sex-stratified models suggest that girls' friendships are more likely given a similar age at menarche. Attention to similar pubertal timing within friendship groups may offer inclusive opportunities for tailored developmental puberty education in ways that reduce stigma and improve health behaviors.

Induction of social contagion for diverse outcomes in structured experiments in isolated villages

Certain people occupy topological positions within social networks that enhance their effectiveness at inducing spillovers. We mapped face-to-face networks among 24,702 people in 176 isolated villages in Honduras and randomly assigned villages to targeting methods, varying the fraction of households receiving a 22-month health education package and the method by which households were chosen (randomly versus using the friendship-nomination algorithm). We assessed 117 diverse knowledge, attitude, and practice outcomes. Friendship-nomination targeting reduced the number of households needed to attain specified levels of village-wide uptake. Knowledge spread more readily than behavior, and spillovers extended to two degrees of separation. Outcomes that were intrinsically easier to adopt also manifested greater spillovers. Network targeting using friendship nomination effectively promotes population-wide improvements in welfare through social contagion.

How Different Uses of Social Media Inform Perceptions of Offline Social Norms and Changes in Vaccine Intentions

As the boundaries between our online and offline lives are increasingly blurred, it is important to attend to the ways in which online behaviors shape our offline perceptions and actions. While a great deal of health communication research has examined the effects of exposure to specific social media content, it is also important to investigate how broader patterns of everyday social media use may affect perceptions of health norms and behaviors in different ways. In this panel survey, we explored how using social media for informational, connection, and aspirational purposes affected perceptions of COVID-19 vaccination norms and changes in vaccine intentions between December 2020 and March 2021 in the US Using social media to seek information and to connect with others positively influenced vaccine intentions via strengthening perceptions of offline social norms supporting vaccination. These findings highlight how social media use is integrated with and informs our offline lives, as well as the important role apolitical social media use plays in shaping attitudes toward politicized science.

Emergence of simple and complex contagion dynamics from weighted belief networks

Social contagion is a ubiquitous and fundamental process that drives individual and social changes. Although social contagion arises as a result of cognitive processes and biases, the integration of cognitive mechanisms with the theory of social contagion remains an open challenge. In particular, studies on social phenomena usually assume contagion dynamics to be either simple or complex, rather than allowing it to emerge from cognitive mechanisms, despite empirical evidence indicating that a social system can exhibit a spectrum of contagion dynamics-from simple to complex-simultaneously. Here, we propose a model of interacting beliefs, from which both simple and complex contagion dynamics can organically arise. Our model also elucidates how a fundamental mechanism of complex contagion-resistance-can come about from cognitive mechanisms.

Compliance with COVID-19 Physical Distancing Mandates in Oman: The Role of Health Literacy and Internal Health Locus of Control

BACKGROUND

Research indicates that the effectiveness of coronavirus disease 2019 (COVID-19) physical distancing mandates is influenced by several individual factors, including health literacy; internal health locus of control (IHLOC), the belief that physical distancing can reduce COVID-19 risk; social norms; self-efficacy; and perceptions of the benefits and barriers associated with distancing. However, further investigation is needed to understand the links between these factors and compliance intentions.

OBJECTIVE

This study investigates the mechanism linking these factors with the intentions to comply with physical distancing mandates.

METHODS

A total of 759 participants (Mean age = 29.13, standard deviation [SD] = 8.33; 68.5% women) were surveyed online from September 2020 to October 2020. Data were analyzed using ANOVA (analysis of variance) and structural equation modeling.

KEY RESULTS

Health literacy was associated with more perceived benefits (β = .175, p = .001), greater self-efficacy (β = .193, p < .001), and less perceived barriers (β = -.391, p < .001). IHLOC was significantly associated with greater perceived benefits (β = .156, p = .007) and self-efficacy (β = .294, p < .001). Family descriptive norms were significantly associated with fewer perceived barriers (β = -.276, p < .001), while injunctive norms were associated with more perceived benefits (β = .202, p = .001) and higher self-efficacy (β = .299, p < .001). Intentions to adhere to physical distancing mandates were significantly associated with past compliance (β = .427, p < .001) and perceived barriers (β = -.205, p < .001) and benefits (β = .295, p < .001). Post-hoc mediation analyses revealed several small yet significant indirect effects, highlighting the complex pathways shaping adherence intentions.

CONCLUSIONS

This study identifies how health literacy, IHLOC, social norms, perceived benefits and barriers, and self-efficacy intricately shape intentions to comply with physical distancing mandates. These findings offer valuable implications for public health policy and interventions. [HLRP: Health Literacy Research and Practice. 2024;8(2):e69-e78.].

Collective Information Seeking During a Health Crisis : Predictors of Google Trends During COVID-19

This article approaches collective health information seeking from computational method by investigating patterns of Google Trends data in the United States during the early stages of the COVID-19 pandemic. We analyzed factors that prompted a community's curiosity, and information that communities were most curious about. The results of our cross-sectional and time-series-based analyses reveal a few salient findings: (1) Republican leaning states searched less frequently, and while states with more cases searched more, partisan lean is a more significant predictor; (2) States with greater level of poverty searched less frequently; (3) Leadership on the national level significantly influenced people's searching behavior; (4) Communities were most interested in "local risk" information as well as quantifiable information. We show in this work that established individual information seeking theoretical predictors (risk) can predict online collective information demand and information seeking subcategories with important contributions from collective conditions (leadership). Health communication practitioners can design health messages and choose media channels more purposefully according to what people are most interested in searching.

Smaldino P, Moser D, Merico A. Noise and opinion dynamics: how ambiguity promotes pro-majority consensus in the presence of confirmation bias

Opinion dynamics are affected by cognitive biases and noise. While mathematical models have focused extensively on biases, we still know surprisingly little about how noise shapes opinion patterns. Here, we use an agent-based opinion dynamics model to investigate the interplay between confirmation bias-represented as bounded confidence-and different types of noise. After analysing where noise can enter social interaction, we propose a type of noise that has not been discussed so far, ambiguity noise. While previously considered types of noise acted on agents either before, after or independent of social interaction, ambiguity noise acts on communicated messages, assuming that socially transmitted opinions are inherently noisy. We find that noise can induce agreement when confirmation bias is moderate, but different types of noise require quite different conditions for this effect to occur. An application of our model to the climate change debate shows that at just the right mix of confirmation bias and ambiguity noise, opinions tend to converge to high levels of climate change concern. This result is not observed with the other types. Our findings highlight the importance of considering and distinguishing between the various types of noise and the unique role of ambiguity in opinion formation.

Collaboration and topic switches in science

Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that scientists choose future research directions via the intertwined mechanisms of selection and social influence. Here we thoroughly investigate the interplay between collaboration and topic switches. We find that the probability for a scholar to start working on a new topic increases with the number of previous collaborators, with a pattern showing that the effects of individual collaborators are not independent. The higher the productivity and the impact of authors, the more likely their coworkers will start working on new topics. The average number of coauthors per paper is also inversely related to the topic switch probability, suggesting a dilution of this effect as the number of collaborators increases.

Social identity bias and communication network clustering interact to shape patterns of opinion dynamics

Social influence aligns people's opinions, but social identities and related in-group biases interfere with this alignment. For instance, the recent rise of young climate activists (e.g. 'Fridays for Future' or 'Last Generation') has highlighted the importance of generational identities in the climate change debate. It is unclear how social identities affect the emergence of opinion patterns, such as consensus or disagreement, in a society. Here, we present an agent-based model to explore this question. Agents communicate in a network and form opinions through social influence. The agents have fixed social identities which involve homophily in their interaction preferences and in-group bias in their perception of others. We find that the in-group bias has opposing effects depending on the network topology. The bias impedes consensus in highly random networks by promoting the formation of echo chambers within social identity groups. By contrast, the bias facilitates consensus in highly clustered networks by aligning dispersed in-group agents across the network and, thereby, preventing the formation of isolated echo chambers. Our model uncovers the mechanisms underpinning these opposing effects of the in-group bias and highlights the importance of the communication network topology for shaping opinion dynamics.

Social networks and voter turnout

This paper develops a model of voter turnout that embeds Riker and Ordeshook's (1968 Am. Pol. Sci. Rev. 62, 25-42 (doi:10.2307/1953324)) calculus of voting into the context of a social network. In the model, an individual's expressive benefits to voting depend on the behaviour of their social contacts. We show that there may be multiple equilibria and analyse how these equilibria depend on the structure of the network. We discuss six empirical implications of the model for turnout, some of which suggest novel answers to longstanding puzzles in the turnout literature, such as: why are higher income individuals more likely to vote even in cases when registration costs are low? Why is turnout so difficult to predict? Why does lowering registration costs disproportionately increase turnout among high-income voters? And why do we observe inertia in turnout across elections?

Unveiling the dynamics of emotions in society through an analysis of online social network conversations

Social networks can provide insights into the emotions expressed by a society. However, the dynamic nature of emotions presents a significant challenge for policymakers, politicians, and communication professionals who seek to understand and respond to changes in emotions over time. To address this challenge, this paper investigates the frequency, duration, and transition of 24 distinct emotions over a 2-year period, analyzing more than 5 million tweets. The study shows that emotions with lower valence but higher dominance and/or arousal are more prevalent in online social networks. Emotions with higher valence and arousal tend to last longer, while dominant emotions tend to have shorter durations. Emotions occupying the conversations predominantly inhibit others with similar valence and dominance, and higher arousal. Over a month, emotions with similar valences tend to prevail in online social network conversations.

Subjective socioeconomic status and income inequality are associated with self-reported morality across 67 countries

Individuals can experience a lack of economic resources compared to others, which we refer to as subjective experiences of economic scarcity. While such experiences have been shown to shift cognitive focus, attention, and decision-making, their association with human morality remains debated. We conduct a comprehensive investigation of the relationship between subjective experiences of economic scarcity, as indexed by low subjective socioeconomic status at the individual level, and income inequality at the national level, and various self-reported measures linked to morality. In a pre-registered study, we analyze data from a large, cross-national survey (N = 50,396 across 67 countries) allowing us to address limitations related to cross-cultural generalizability and measurement validity in prior research. Our findings demonstrate that low subjective socioeconomic status at the individual level, and income inequality at the national level, are associated with higher levels of moral identity, higher morality-as-cooperation, a larger moral circle, and increased prosocial intentions. These results appear robust to several advanced control analyses. Finally, exploratory analyses indicate that observed income inequality at the national level is not a statistically significant moderator of the associations between subjective socioeconomic status and the included measures of morality. These findings have theoretical and practical implications for understanding human morality under experiences of resource scarcity.

Impact of Social Reference Cues on Misinformation Sharing on Social Media: Series of Experimental Studies

BACKGROUND

Health-related misinformation on social media is a key challenge to effective and timely public health responses. Existing mitigation measures include flagging misinformation or providing links to correct information, but they have not yet targeted social processes. Current approaches focus on increasing scrutiny, providing corrections to misinformation (debunking), or alerting users prospectively about future misinformation (prebunking and inoculation). Here, we provide a test of a complementary strategy that focuses on the social processes inherent in social media use, in particular, social reinforcement, social identity, and injunctive norms.

OBJECTIVE

This study aimed to examine whether providing balanced social reference cues (ie, cues that provide information on users sharing and, more importantly, not sharing specific content) in addition to flagging COVID-19-related misinformation leads to reductions in sharing behavior and improvement in overall sharing quality.

METHODS

A total of 3 field experiments were conducted on Twitter's native social media feed (via a newly developed browser extension). Participants' feed was augmented to include misleading and control information, resulting in 4 groups: no-information control, Twitter's own misinformation warning (misinformation flag), social cue only, and combined misinformation flag and social cue. We tracked the content shared or liked by participants. Participants were provided with social information by referencing either their personal network on Twitter or all Twitter users.

RESULTS

A total of 1424 Twitter users participated in 3 studies (n=824, n=322, and n=278). Across all 3 studies, we found that social cues that reference users' personal network combined with a misinformation flag reduced the sharing of misleading but not control information and improved overall sharing quality. We show that this improvement could be driven by a change in injunctive social norms (study 2) but not social identity (study 3).

CONCLUSIONS

Social reference cues combined with misinformation flags can significantly and meaningfully reduce the amount of COVID-19-related misinformation shared and improve overall sharing quality. They are a feasible and scalable way to effectively curb the sharing of COVID-19-related misinformation on social media.

Four errors and a fallacy: pitfalls for the unwary in comparative brain analyses

Comparative analyses are the backbone of evolutionary analysis. However, their record in producing a consensus has not always been good. This is especially true of attempts to understand the factors responsible for the evolution of large brains, which have been embroiled in an increasingly polarised debate over the past three decades. We argue that most of these disputes arise from a number of conceptual errors and associated logical fallacies that are the result of a failure to adopt a biological systems-based approach to hypothesis-testing. We identify four principal classes of error: a failure to heed Tinbergen's Four Questions when testing biological hypotheses, misapplying Dobzhansky's Dictum when testing hypotheses of evolutionary adaptation, poorly chosen behavioural proxies for underlying hypotheses, and the use of inappropriate statistical methods. In the interests of progress, we urge a more careful and considered approach to comparative analyses, and the adoption of a broader, rather than a narrower, taxonomic perspective.

Identifying Influential Nodes in Social Networks: Exploiting Self-Voting Mechanism

The influence maximization (IM) problem is defined as identifying a group of influential nodes in a network such that these nodes can affect as many nodes as possible. Due to its great significance in viral marketing, disease control, social recommendation, and so on, considerable efforts have been devoted to the development of methods to solve the IM problem. In the literature, VoteRank and its improved algorithms have been proposed to select influential nodes based on voting approaches. However, in the voting process of these algorithms, a node cannot vote for itself. We argue that this voting schema runs counter to many real scenarios. To address this issue, we designed the VoteRank* algorithm, in which we first introduce the self-voting mechanism into the voting process. In addition, we also take into consideration the diversities of nodes. More explicitly, we measure the voting ability of nodes and the amount of a node voting for its neighbors based on the H-index of nodes. The effectiveness of the proposed algorithm is experimentally verified on 12 benchmark networks. The results demonstrate that VoteRank* is superior to the baseline methods in most cases.

Long ties, disruptive life events, and economic prosperity

Social networks shape and reflect economic life. Prior studies have identified long ties, which connect people who lack mutual contacts, as a correlate of individuals' success within firms and places' economic prosperity. However, we lack population-scale evidence of the individual-level link between long ties and economic prosperity, and why some people have more long ties remains obscure. Here, using a social network constructed from interactions on Facebook, we establish a robust association between long ties and economic outcomes and study disruptive life events hypothesized to cause formation of long ties. Consistent with prior aggregated results, administrative units with a higher fraction of long ties tend to have higher-income and economic mobility. Individuals with more long ties live in higher-income places and have higher values of proxies for economic prosperity (e.g., using more Internet-connected devices and making more donations). Furthermore, having stronger long ties (i.e., with higher intensity of interaction) is associated with better outcomes, consistent with an advantage from the structural diversity constituted by long ties, rather than them being weak ties per se. We then study the role of disruptive life events in the formation of long ties. Individuals who have migrated between US states, have transferred between high schools, or have attended college out-of-state have a higher fraction of long ties among their contacts many years after the event. Overall, these results suggest that long ties are robustly associated with economic prosperity and highlight roles for important life experiences in developing and maintaining long ties.

The impact of ethnic segregation on neighbourhood-level social distancing in the United States amid the early outbreak of COVID-19

The COVID-19 pandemic has been argued to be the 'great equaliser', but, in fact, ethnically and racially segregated communities are bearing a disproportionate burden from the disease. Although more people have been infected and died from the disease among these minority communities, still fewer people in these communities are complying with the suggested public health measures like social distancing. The factors contributing to these ramifications remain a long-lasting debate, in part due to the contested theories between ethnic stratification and ethnic community. To offer empirical evidence to this theoretical debate, we tracked public social-distancing behaviours from mobile phone devices across urban census tracts in the United States and employed a difference-in-difference model to examine the impact of racial/ethnic segregation on these behaviours. Specifically, we focussed on non-Hispanic Black and Hispanic communities at the neighbourhood level from three principal dimensions of ethnic segregation, namely, evenness, exposure, and concentration. Our results suggest that (1) the high ethnic diversity index can decrease social-distancing behaviours and (2) the high dissimilarity between ethnic minorities and non-Hispanic Whites can increase social-distancing behavior; (3) the high interaction index can decrease social-distancing behaviours; and (4) the high concentration of ethnic minorities can increase travel distance and non-home time but decrease work behaviours. The findings of this study shed new light on public health behaviours among minority communities and offer empirical knowledge for policymakers to better inform just and evidence-based public health orders.

Negativity drives online news consumption

Online media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here we analyse the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743). Our dataset comprises ~105,000 different variations of news stories from Upworthy.com that generated ∼5.7 million clicks across more than 370 million overall impressions. Although positive words were slightly more prevalent than negative words, we found that negative words in news headlines increased consumption rates (and positive words decreased consumption rates). For a headline of average length, each additional negative word increased the click-through rate by 2.3%. Our results contribute to a better understanding of why users engage with online media.

Not All Effects Are Indispensable: Psychological Science Requires Verifiable Lines of Reasoning for Whether an Effect Matters

To help move researchers away from heuristically dismissing "small" effects as unimportant, recent articles have revisited arguments to defend why seemingly small effect sizes in psychological science matter. One argument is based on the idea that an observed effect size may increase in impact when generalized to a new context because of processes of accumulation over time or application to large populations. However, the field is now in danger of heuristically accepting all effects as potentially important. We aim to encourage researchers to think thoroughly about the various mechanisms that may both amplify and counteract the importance of an observed effect size. Researchers should draw on the multiple amplifying and counteracting mechanisms that are likely to simultaneously apply to the effect when that effect is being generalized to a new and likely more dynamic context. In this way, researchers should aim to transparently provide verifiable lines of reasoning to justify their claims about an effect's importance or unimportance. This transparency can help move psychological science toward a more rigorous assessment of when psychological findings matter for the contexts that researchers want to generalize to.

Did you donate? Talking about donations predicts compliance with solicitations for donations

Many forms of prosocial behaviour are highly institutionalized. They are facilitated by organizations that broker between donors and recipients. A highly effective tool that organizations use to elicit prosocial behaviour are solicitations for donations (e.g., of blood, time, or money). Using register and survey data on blood donations in the Netherlands, we examine to what extent compliance with these solicitations is predicted by being recruited via word of mouth (WOM) and talking about donations. Our model predicts that donors that are one unit higher on our measure of talking about donations (range = 1-4) have a 2.9 percentage points higher compliance with solicitations for donations. In addition, this association is stronger for novice donors. Our study demonstrates the social embedding of the donors' decision-making processes about compliance. For practice, our results imply that organizations may increase their contributors' communication about donations to increase the effectiveness of their solicitations.

Let's Give Together: Can Collaborative Giving Boost Generosity?

A growing number of people donate to charity together with others, such as a spouse, friend, or stranger. Does giving to charity collectively with another person-called collaborative giving-promote generosity? Existing data offer unsatisfactory insight; most studies are correlational, present mixed findings, or examine other concepts. Yet, theory suggests that collaborative giving may increase generosity because giving with others could be intrinsically enjoyable. We conducted two well-powered, pre-registered experiments to test whether collaborative giving boosts generosity. In Experiment 1 (N = 202; 101 dyads) and Experiment 2 (N = 310; 155 dyads), pairs of unacquainted undergraduates earned money and were randomly assigned to donate collaboratively (Experiments 1-2), individually in each other's presence (Experiments 1-2), or privately (Experiment 2). Across studies, we observed no condition differences on generosity. However, collaborative (vs. individual) giving predicted greater intrinsic enjoyment, which, in turn, predicted larger donations, suggesting a promising potential mechanism for future research and practice.

Knowledge Discovery: Methods from data mining and machine learning

The interdisciplinary field of knowledge discovery and data mining emerged from a necessity of big data requiring new analytical methods beyond the traditional statistical approaches to discover new knowledge from the data mine. This emergent approach is a dialectic research process that is both deductive and inductive. The data mining approach automatically or semi-automatically considers a larger number of joint, interactive, and independent predictors to address causal heterogeneity and improve prediction. Instead of challenging the conventional model-building approach, it plays an important complementary role in improving model goodness of fit, revealing valid and significant hidden patterns in data, identifying nonlinear and non-additive effects, providing insights into data developments, methods, and theory, and enriching scientific discovery. Machine learning builds models and algorithms by learning and improving from data when the explicit model structure is unclear and algorithms with good performance are difficult to attain. The most recent development is to incorporate this new paradigm of predictive modeling with the classical approach of parameter estimation regressions to produce improved models that combine explanation and prediction.

Debating stereotypes: Online reactions to the vice-presidential debate of 2020

The 2020 Vice-Presidential debate afforded the opportunity to examine online reactions toward a woman of color, Kamala Harris, and a white man, Mike Pence, as they vied for the same position. We collected tweets from the Twitter API related to Harris and Pence, mainly using neutral hashtags. We examined keywords for gender and race slurs and conducted a multivariate analysis of tweet sentiment. Gender and racial slurs surface in both Harris and Pence datasets, showcasing the insidious nature of sexist and racist stereotypes that seep into online conversations regarding a high-status job debate. As anticipated, tweets regarding Harris contained a higher proportion of racist and sexist curse words, highlighting greater levels of harassment and "intersectional," multi-ethnic/gender attacks. Racial insults targeting Blacks or Asians were more negative than those associated with Whites. Unexpectedly, tweets related to Harris were more positive in average sentiment than those regarding Pence. Yet, there were significantly more retweets, and more negativity of retweets, relating to Harris than to Pence, underscoring the relatively widespread broadcasting of derogatory messages about Harris. Overall, we found that harassing messages toward the candidates reinforced traditional race and gender stereotypes and bolstered the status of those who posted negative content by attaining more retweets. Harassers routinely invoked well-worn, stereotypical insults in their attacks, especially when targeting a multiracial woman.

On limitations of uniplex networks for modeling multiplex contagion

Many network contagion processes are inherently multiplex in nature, yet are often reduced to processes on uniplex networks in analytic practice. We therefore examine how data modeling choices can affect the predictions of contagion processes. We demonstrate that multiplex contagion processes are not simply the union of contagion processes over their constituent uniplex networks. We use multiplex network data from two different contexts-(1) a behavioral network to represent their potential for infectious disease transmission using a "simple" epidemiological model, and (2) users from online social network sites to represent their potential for information spread using a threshold-based "complex" contagion process. Our results show that contagion on multiplex data is not captured accurately in models developed from the uniplex networks even when they are combined, and that the nature of the differences between the (combined) uniplex and multiplex results depends on the specific spreading process over these networks.

The link between reported cases of COVID-19 and the Infodemic Risk Index: A worldwide perspective

In this brief report we followed the evolution of the COVID-19 Infodemic Risk Index during 2020 and clarified its connection with the epidemic waves, focusing specifically on their co-evolution in Europe, South America, and South-eastern Asia. Using 640 million tweets collected by the Infodemic Observatory and the open access dataset published by Our World in Data regarding COVID-19 worldwide reported cases, we analyze the COVID-19 infodemic vs. pandemic co-evolution from January 2020 to December 2020. We find that a characteristic pattern emerges at the global scale: a decrease in misinformation on Twitter as the number of COVID-19 confirmed cases increases. Similar local variations highlight how this pattern could be influenced both by the strong content moderation policy enforced by Twitter after the first pandemic wave and by the phenomenon of selective exposure that drives users to pick the most visible and reliable news sources available.

People conform to social norms when gambling with lives or money

Many consider moral decisions to follow an internal "moral compass", resistant to social pressures. Here we examine how social influence shapes moral decisions under risk, and how it operates in different decision contexts. We employed an adapted Asian Disease Paradigm where participants chose between certain losses/gains and probabilistic losses/gains in a series of moral (lives) or financial (money) decisions. We assessed participants' own risk preferences before and after exposing them to social norms that are generally risk-averse or risk-seeking. Our results showed that participants robustly shifted their own choices towards the observed risk preferences. This conformity holds even after a re-testing in three days. Interestingly, in the monetary domain, risk-averse norms have more influence on choices in the loss frame, whereas risk-seeking norms have more influence in the gain frame, presumably because norms that contradict default behavior are most informative. In the moral domain, risk-averse as opposed to risk-seeking norms are more effective in the loss frame but in the gain frame different norms are equally effective. Taken together, our results demonstrate conformity in risk preferences across contexts and highlight unique features of decisions and conformity in moral and monetary domains.

AI-deploying organizations are key to addressing 'perfect storm' of AI risks

We argue that a perfect storm of five conditions heightens the risk of harm to society from artificial intelligence: (1) the powerful, invisible nature of AI, (2) low public awareness and AI literacy, (3) rapid scaled deployment of AI, (4) insufficient regulation, and (5) the gap between trustworthy AI principles and practices. To prevent harm, fit-for-purpose regulation and public AI literacy programs have been recommended, but education and government regulation will not be sufficient: AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and taking accountability to mitigate the risks.

A systematic review of worldwide causal and correlational evidence on digital media and democracy

One of today's most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.

Homophily and polarization on political twitter during the 2017 Norwegian election

Ideological homophily on social media has been receiving increased scholarly interest, as it is associated with the formation of filter bubbles, echo chambers, and increased ideological polarization. And yet, no linkage necessarily exists between ideological homophily, echo chambers, and polarization. Despite political interactions on social media taking place to a large extent between like-minded individuals, cross-cutting interactions are also frequent. Using Twitter data, we investigated the extent to which ideological homophily, echo chambers, and polarization occur together and characterize the network of political Twitter users during the 2017 election in Norway. Despite the presence of some degree of ideological homophily, we did not find evidence of echo chambers in the Norwegian political Twittersphere during the 2017 election. And yet, the retweet network is characterized by a significant degree of polarization across ideological blocs. Our findings support the thesis according to which polarization on social media may have drivers other than the technological deterministic effect of social media affordances enhancing the formation of online echo chambers.

Social Networks, Health Information Sharing, and Pandemic Perceptions among Young Adults in Hawai'i during the COVID-19 Pandemic

Limited information exists about social network variation and health information sharing during COVID-19, especially for Native Hawaiians (NH), Other Pacific Islanders (OPI), and Filipinos, who experienced COVID-19 inequities. Hawai'i residents aged 18-35 completed an online survey regarding social media sources of COVID-19 information and social network health information measured by how many people participants: (1) talked to and (2) listened to about health. Regression models were fit with age, gender, race/ethnicity, chronic disease status, pandemic perceptions, and health literacy as predictors of information sources (logistic) and social network size (Poisson). Respondents were 68% female; 41% NH, OPI, or Filipino; and 73% conducted a recent COVID-19 digital search for themselves or others. Respondents listened to others or discussed their own health with ~2-3 people. Respondents who talked with more people about their health were more likely to have larger networks for listening to others. In regression models, those who perceived greater risk of acquiring COVID-19 discussed their health with more people; in discussing others' health, women and those with chronic diseases listened to a greater number. Understanding young adults' social networks and information sources is important for health literacy and designing effective health communications, especially to reach populations experiencing health inequities.

Promoting blood donation through social media: Evidence from Brazil, India and the USA

Social media has the potential to encourage prosocial behaviors at scale, yet very little causal evidence exists on the impact of related efforts. Blood donation is a particularly difficult, but essential prosocial behavior that is often critically undersupplied. We examine the effect of Facebook's blood donation tool on voluntary blood donation. We partnered with four major blood banks in the United States covering 363 collection facilities in 46 states and Washington, D.C. We tracked the tool's impact on blood donations during its staggered rollout on a sample of more than 47,000 facility-date observations from March 2019 to September 2019. The tool caused an increase of 0.55 total donations per facility per day (+4.0% [95% CI: 0.04%-8.0%]), and an increase of 0.15 donations from first-time donors per facility per day (+18.9% [95% CI: 4.7%-33.1%]). Longitudinal evidence from Brazil and India suggests the share of donors who both received a message from the tool and stated they were influenced by Facebook to donate increased from 0% to 14.1% [95% CI: 12.1%-16.2%] in the first year of the tool's deployment (i.e., September 2018 to August 2019). These meaningful increases, especially from first-time donors, demonstrate that social media platforms can play an important role in fostering offline prosocial behaviors that benefit the health and well-being of societies around the world.

Social information decreases giving in late-stage fundraising campaigns

Online fundraisers often showcase information about the number of donations received and the proximity to the campaign goal. This practice follows research on descriptive norms and goal-directed motivation, which predicts higher contributions as the number of donors increases and as the campaign goal is approached. However, across three studies, we demonstrate that when the campaign is close to completion, individuals give more when they see that there are few (vs. many) donors to the campaign. We observe this result across real campaigns on a fundraising website and obtain causal evidence for this effect in two laboratory experiments. We find that this effect is driven in part by an increase in the perceived progress that one's donation makes towards reaching the campaign goal. This work identifies a counterintuitive consequence of norm-based marketing appeals and has important implications for fundraisers.

Structural Cohesion, Role Equivalence, or Homophily: Which Process Best Explains Social Homogeneity?

Social homogeneity, understood as the similarity of perceptions and attitudes that individuals display toward the environment around them, is explained by the relational context in which they are immersed. However, there is no consensus about which relational mechanism best explains social homogeneity. The purpose of this research is to find out which of the three classical relational processes most studied in network analysis (structural cohesion, role equivalence, or homophily) is more determinant in explaining social homogeneity. To achieve the research objective, 110 professionals (psychologists, social workers, and community facilitators) implementing a psychosocial care program in three regions of Northwest Colombia were interviewed. Different types of relationships among professionals were analyzed using network analysis techniques. To examine the structural cohesion hypothesis, interveners were categorized according to the level of structural cohesion by performing core-periphery analysis in the networks evaluated; to test the role equivalence hypothesis, participants were categorized according to their level of degree centrality in the networks examined; to test the homophily hypothesis, participants were grouped according to the level of homophily in terms of professional profile. The non-parametric tests showed that role equivalence was the most powerful mechanism for explaining social homogeneity in the sample of psychosocial interveners evaluated.

A cultural evolutionary theory that explains both gradual and punctuated change

Cumulative cultural evolution (CCE) occurs among humans who may be presented with many similar options from which to choose, as well as many social influences and diverse environments. It is unknown what general principles underlie the wide range of CCE dynamics and whether they can all be explained by the same unified paradigm. Here, we present a scalable evolutionary model of discrete choice with social learning, based on a few behavioural science assumptions. This paradigm connects the degree of transparency in social learning to the human tendency to imitate others. Computer simulations and quantitative analysis show the interaction of three primary factors-information transparency, popularity bias and population size-drives the pace of CCE. The model predicts a stable rate of evolutionary change for modest degrees of popularity bias. As popularity bias grows, the transition from gradual to punctuated change occurs, with maladaptive subpopulations arising on their own. When the popularity bias gets too severe, CCE stops. This provides a consistent framework for explaining the rich and complex adaptive dynamics taking place in the real world, such as modern digital media.