The diffusion of culture online is theorized to be influenced by many interacting social factors (e.g., network and identity).However, most existing computational cascade models consider just a single factor (e.g., network or identity).This work offers a new framework for teasing apart the mechanisms underlying hashtag cascades.We curate a new dataset of 1,337 hashtags representing cultural innovation online, develop a 10-factor evaluation framework for comparing empirical and simulated cascades, and show that a combined network+identity model better simulates hashtag cascades than network-or identity-only counterfactuals.We also explore heterogeneity in performance: While a combined network+identity model best predicts the popularity of cascades, a network-only model best predicts cascade growth and an identity-only model best predicts adopter composition.The network+identity model has the highest comparative advantage among hashtags used for expressing racial or regional identity and talking about sports or news.In fact, we are able to predict what combination of network and/or identity best models each hashtag and use this to further improve performance.Our results show the utility of models incorporating the interactions of network, identity, and other social factors in the diffusion of hashtags in social media.

Town hall-type debates are increasingly moving online, irrevocably transforming public discourse. Yet, we know relatively little about crucial social dynamics that determine which arguments are more likely to be successful. This study investigates the impact of one's position in the discussion network created via responses to others' arguments on one's persuasiveness in unfacilitated online debates. We propose a novel framework for measuring the relationship between network position and persuasiveness, using a combination of social network analysis and machine learning. Complementing existing studies investigating the effect of linguistic aspects on persuasiveness, we show that the user's position in a discussion network is associated with their persuasiveness online. Moreover, the recognition of successful persuasion is linked to an increase in dominant network position. Our findings offer important insights into the complex social dynamics of online discourse and provide practical insights for organizations and individuals seeking to understand the interplay between influential positions in a discussion network and persuasive strategies in digital spaces.

Amidst growing uncertainty and frequent restructurings, the impacts of employee exits are becoming one of the central concerns for organizations. Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. Specifically, we investigate how network metrics change among people who historically interacted with departing employees. We find evidence of "breakdown" in communication among the remaining coworkers, who tend to become less connected with fewer interactions after their coworkers' departure. This effect appears to be moderated by both external factors, such as periods of high organizational stress, and internal factors, such as the characteristics of the departing employee. At the external level, periods of high stress correspond to greater communication breakdown; at the internal level, however, we find patterns suggesting individuals may end up better positioned in their networks after a network neighbor's departure. Overall, our study provides critical insights into managing workforce changes and preserving communication dynamics in the face of employee exits.

Individuals experiencing unexpected distressing events, shocks, often rely on their social network for support. While prior work has shown how social networks respond to shocks, these studies usually treat all ties equally, despite differences in the support provided by different social relationships. Here, we conduct a computational analysis on Twitter that examines how responses to online shocks differ by the relationship type of a user dyad. We introduce a new dataset of over 13K instances of individuals' self-reporting shock events on Twitter and construct networks of relationship-labeled dyadic interactions around these events. By examining behaviors across 110K replies to shocked users in a pseudo-causal analysis, we demonstrate relationship-specific patterns in response levels and topic shifts. We also show that while well-established social dimensions of closeness such as tie strength and structural embeddedness contribute to shock responsiveness, the degree of impact is highly dependent on relationship and shock types. Our findings indicate that social relationships contain highly distinctive characteristics in network interactions, and that relationship-specific behaviors in online shock responses are unique from those of offline settings.

The public interest in accurate scientific communication, underscored by recent public health crises, highlights how content often loses critical pieces of information as it spreads online. However, multi-platform analyses of this phenomenon remain limited due to challenges in data collection. Collecting mentions of research tracked by Altmetric LLC, we examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles across blog sites, Facebook, news sites, Twitter, and Wikipedia. To do so, we present a burst-based framework for examining online discussions about science over time and across different platforms. To measure information retention, we develop a keyword-based computational measure comparing an online post to the scientific article's abstract. We evaluate our measure using ground truth data labeled by within field experts. We highlight three main findings: first, we find a strong tendency towards low levels of information retention, following a distinct trajectory of loss except when bursts of attention begin in social media. Second, platforms show significant differences in information retention. Third, sequences involving more platforms tend to be associated with higher information retention. These findings highlight a strong tendency towards information loss over time---posing a critical concern for researchers, policymakers, and citizens alike---but suggest that multi-platform discussions may improve information retention overall.

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected all 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.

Collaborative crowdsourcing is a well-established model of work, especially in the case of open source software development. The structure and operation of these virtual and loosely-knit teams differ from traditional organizations. As such, little is known about how their behavior may change in response to an increase in external attention. To understand these dynamics, we analyze millions of actions of thousands of contributors in over 1100 open source software projects that topped the GitHub Trending Projects page and thus experienced a large increase in attention, in comparison to a control group of projects identified through propensity score matching. In carrying out our research, we use the lens of organizational change, which considers the challenges teams face during rapid growth and how they adapt their work routines, organizational structure, and management style. We show that trending results in an explosive growth in the effective team size. However, most newcomers make only shallow and transient contributions. In response, the original team transitions towards administrative roles, responding to requests and reviewing work done by newcomers. Projects evolve towards a more distributed coordination model with newcomers becoming more central, albeit in limited ways. Additionally, teams become more modular with subgroups specializing in different aspects of the project. We discuss broader implications for collaborative crowdsourcing teams that face attention shocks.

The proliferation of online communities has created exciting opportunities to study the mechanisms that explain group success. While a growing body of research investigates community success through a single measure - typically, the number of members - we argue that there are multiple ways of measuring success. Here, we present a systematic study to understand the relations between these success definitions and test how well they can be predicted based on community properties and behaviors from the earliest period of a community's lifetime. We identify four success measures that are desirable for most communities: (i) growth in the number of members; (ii) retention of members; (iii) long term survival of the community; and (iv) volume of activities within the community. Surprisingly, we find that our measures do not exhibit very high correlations, suggesting that they capture different types of success. Additionally, we find that different success measures are predicted by different attributes of online communities, suggesting that success can be achieved through different behaviors. Our work sheds light on the basic understanding on what success represents in online communities and what predicts it. Our results suggest that success is multi-faceted and cannot be measured nor predicted by a single measurement. This insight has practical implications for the creation of new online communities and the design of platforms that facilitate such communities.

Online social movements (OSMs) play a key role in promoting democracy in modern society. Most online activism is largely driven by critical offline events. Among many studies investigating collective behavior in OSMs, few has explored the interaction between crowd dynamics and their offline context. Here, focusing on the Black Lives Matter OSM and utilizing an event-driven approach on a dataset of 36 million tweets and thousands of offline events, we study how different types of offline events-police violence and heightened protests-influence crowd behavior over time. We find that police violence events and protests play important roles in the recruitment process. Moreover, by analyzing the re-participation dynamics and patterns of social interactions, we find that, in the long term, users who joined the movement during police violence events and protests show significantly more commitment than those who joined during other times. However, users recruited during other times are more committed to the movement than the other two groups in the short term. Furthermore, we observe that social ties formed during police violence events are more likely to be sustained over time than those formed during other times. Contrarily, ties formed during protests are the least likely to be maintained. Altogether, our results shed light on the impact of bursting events on the recruitment, retention, and communication patterns of collective behavior in the Black Lives Matter OSM.

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network’s structure and communicative properties. Here, we study how external events are associated with a network’s change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. We also analyze the communication dynamics among specialized teams in the organization. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness such as the activation of weak ties to obtain novel information. Rather, the network “turtles up.” It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider and within-role vs. between-role communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

Sequential group decision-making processes, such as online event scheduling, can be subject to social influence if the decisions involve individuals? subjective preferences and values. Indeed, prior work has shown that scheduling polls that allow respondents to see others' answers are more likely to succeed than polls that hide other responses, suggesting the impact of social influence and coordination. In this paper, we investigate whether this difference is due to information cascade effects in which later respondents adopt the decisions of earlier respondents. Analyzing more than 1.3 million Doodle polls, we found evidence that cascading effects take place during event scheduling, and in particular, that early respondents have a larger influence on the outcome of a poll than people who come late. Drawing on simulations of an event scheduling model, we compare possible interventions to mitigate this bias and show that we can optimize the success of polls by hiding the responses of a small percentage of low availability respondents.

One advantage of crowds over traditional teams is that crowds enable the assembling of a large number of individuals to address problems. The literature is unclear, however, about when crowd size leads to better outcomes. To better understand the effects of crowd size we conducted a study on the retention and performance of 4,317 articles in the WikiProject Film community. Results indicate that crowd composition, specifically diversity and experience, is vital to understanding when size leads to better retention and performance. Crowd size was positively related to retention and performance when crowds were high in diversity and experience. Retention was important to determining when crowd size led to better performance. Crowd size was positively related to performance when retention was low. Our results suggest that crowds benefit from their size when they are diverse, experienced, and have low retention rates.

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network's structure and communicative properties. Here, we study how external events are associated with a network's change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness. Rather, the network 'turtles up'. It displays a propensity for higher clustering, strong tie inter- action, and an intensification of insider vs. outsider communication. Further, we find changes in network structure pre- dict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

Link structure in online networks carries varying semantics. For example, Facebook links carry social semantics while LinkedIn links carry professional semantics. It has been shown that online networks are useful for predicting users' future activities. In this paper, we introduce a new related problem: given a collection of networks, how can we determine the relative importance of each network for predicting user activities? We propose a framework that allows us to quantify the relative predictive value of each network in a setting where multiple networks are available. We give an ɛ-net algorithm to solve the problem and prove that it finds a solution that is arbitrarily close to the optimal solution. Experimentally, we focus our study on the prediction of ad clicks, where it is already known that a single social network improves prediction. The networks we study are implicit affiliations networks, which are based on users' browsing history rather than declared relationships between the users. We create two networks based on covisitation to pages in the Facebook domain and Wikipedia domain. The learned relative weighting of these networks demonstrates covisitation networks are indeed useful for prediction, but that no single network is predictive of all kinds of ads. Rather, each category of ads calls for a significantly different weighting of these networks.

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

There is a widespread intuitive sense that different kinds of information spread differently on-line, but it has been difficult to evaluate this question quantitatively since it requires a setting where many different kinds of information spread in a shared environment. Here we study this issue on Twitter, analyzing the ways in which tokens known as hashtags spread on a network defined by the interactions among Twitter users. We find significant variation in the ways that widely-used hashtags on different topics spread.