Mohsen Mosleh, Rocky Cole, David G Rand Author Notes
PNAS Nexus, Volume 3, Issue 3, March 2024, pgae111,

There is considerable concern about users posting misinformation and harmful language on social media. Substantial—yet largely distinct—bodies of research have studied these two kinds of problematic content. Here, we shed light on both research streams by examining the relationship between the sharing of misinformation and the use of harmful language. We do so by creating and analyzing a dataset of 8,687,758 posts from N = 6,832 Twitter (now called X) users, and a dataset of N = 14,617 true and false headlines from professional fact-checking websites. Our analyses reveal substantial positive associations between misinformation and harmful language. On average, Twitter posts containing links to lower-quality news outlets also contain more harmful language (β = 0.10); and false headlines contain more harmful language than true headlines (β = 0.19). Additionally, Twitter users who share links to lower-quality news sources also use more harmful language—even in non-news posts that are unrelated to (mis)information (β = 0.13). These consistent findings across different datasets and levels of analysis suggest that misinformation and harmful language are related in important ways, rather than being distinct phenomena. At the same, however, the strength of associations is not sufficiently high to make the presence of harmful language a useful diagnostic for information quality: most low-quality information does not contain harmful language, and a considerable fraction of high-quality information does contain harmful language. Overall, our results underscore important opportunities to integrate these largely disconnected strands of research and understand their psychological connections.

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Xiao Yang, Réka Albert, Lauren Molloy Elreda, & Nilam Ram

Social influence processes can induce desired or undesired behavior change in individual members of a group. Empirical modeling of group processes and the design of network-based interventions meant to promote desired behavior change is somewhat limited be-cause the models often assume that the social influence is assimilative only and that the networks are not fully connected. We introduce a Boolean network method that addresses these two limitations. In line with dynamical systems principles, temporal changes in group members’ behavior are modeled as a Boolean network that also allows for application of control theory design of group management strategies that might direct the groups to-wards desired behavior. To illustrate the utility of the method for psychology, we apply the Boolean network method to empirical data of individuals’ self-disclosure behavior in multi-week therapy groups (N = 135, 18 groups, T = 10 ∼ 16 weeks). Empirical results provide descrip-tion of each group member’s pattern of self-disclosure and social influence and identification of group-specific network control strategies that would elicit self-disclosure from the majori-ty of the group. Of the 18 group models, 16 included both assimilative and repulsive social in-fluence. Useful control strategies were not needed for 10 already well-functioning groups, were identified for 6 groups, and were not available for 2 groups. The findings illustrate the utility of the Boolean network method for modeling the simultaneous existence of assimila-tive and repulsive social influence processes in small groups, and developing strategies that may direct groups toward desired states without manipulating social ties.

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Francis Heylighen, Shima Beigi, Clement Vidal

This report is a first survey of a new, evolutionary narrative, called the Third Story, intended to replace and complement the earlier religious (First) and mechanistic (Second) worldviews. We first argue that the confusions created by a world that is ever more volatile, uncertain, complex and ambiguous (VUCA) have eroded people’s sense of coherence, that is, the degree to which they experience the world as comprehensible, manageable and meaningful. The First Story provides meaning and values, but its descriptions no longer provide an accurate understanding of how the universe functions. The Second Story, which sees the universe as a clockwork mechanism governed by the laws of nature, provides more accurate predictions that allow us to build powerful technologies. However, it does not provide meaning or values. The Third Story sees the universe as self-organizing towards increasing complexity and consciousness, subsequently producing matter, life, mind and society. It understands the fundamental mechanism of evolution as mutual adaptation or “fit” between interacting systems, thus generating synergetic wholes that in turn interact, so as integrate into even more complex wholes. Its implicit value is the search for fitness and synergy, thus inviting individuals to work towards a further integration of the noosphere, i.e. the planetary superorganism formed by humanity, its technological extensions, and the ecosystem.

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HONGZHONG DENG, JI LI, HONGQIAN WU, and BINGFENG GE

Advances in Complex SystemsVol. 26, No. 07n08, 2350011

System structure can affect or decide the system function. Many pioneers have analyzed the impact of system’s macro-statistical characteristics, such as degree distribution and giant component, on system performance. But only few research works were conducted on the relation of mesoscopic structure and agent property with system task performance. In this paper, we designed a scenario that, in a multiagent system, agents will try their best to form a qualified team to fulfill more system tasks under the requirements from agent property, structure and task. The theoretical and simulation results show that the agent link network, agent properties and task requirement will co-affect the dynamic team formation and at last have serious effects on a system’s task completion ratio and performance. Some factors such as network density and task introduction period have positive influence. Task execution time and team size have negative influence. Some factors show a counter-intuitive influence. The clustering coefficient has not much influence as people expected and the task publicity time isn’t bigger the better. Notably, system performance is affected by the coupling effect, instead of the independent effects of all factors. The effect of system structure on system function conditionally relies on the support from agent ability and task requirement.

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Michele Avalle, Niccolò Di Marco, Gabriele Etta, Emanuele Sangiorgio, Shayan Alipour, Anita Bonetti, Lorenzo Alvisi, Antonio Scala, Andrea Baronchelli, Matteo Cinelli & Walter Quattrociocchi 
Nature (2024)

Growing concern surrounds the impact of social media platforms on public discourse1,2,3,4 and their influence on social dynamics5,6,7,8,9, especially in the context of toxicity10,11,12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years—from Usenet to contemporary social media—our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.

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