Proactive content moderation requires platforms to rapidly and continuously evaluate the credibility of websites. Leveraging the direct and indirect paths users follow to unreliable websites, we develop a website credibility classification and discovery system that integrates both webgraph and large-scale social media contexts. We additionally introduce the concept of dredge words—terms or phrases for which unreliable domains rank highly on search engines—and provide the first exploration of their usage on social media. Our graph neural networks that combine webgraph and social media contexts generate to state-of-the-art results in website credibility classification and significantly improves the top-k identification of unreliable domains. Additionally, we release a novel dataset of dredge words, highlighting their strong connections to both social media and online commerce platforms.

The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to reliable domains. We build these interventions on the principles of fairness (penalize sites for what is in their control), generality (label/fact-check agnostic), targeted (increase the cost of adversarial behavior), and scalability (works at webscale). We refine our methods on small-scale webdata as a testbed and then generalize the interventions to a large-scale webgraph containing 93.9M domains and 1.6B edges. We demonstrate that our methods penalize unreliable domains far more than reliable domains in both settings and we explore multiple avenues to mitigate unintended effects on both the small-scale and large-scale webgraph experiments. These results indicate the potential of our approach to reduce the spread of misinformation and foster a more reliable online information ecosystem. This research contributes to the development of targeted strategies to enhance the trustworthiness and quality of search engine results, ultimately benefiting users, and the broader digital community.

Website reliability labels underpin almost all research in misinformation detection. However, misinformation sources often exhibit transient behavior, which makes many such labeled lists obsolete over time. We demonstrate that Search Engine Optimization (SEO) attributes provide strong signals for predicting news site reliability. We introduce a novel attributed webgraph dataset with labeled news domains and their connections to outlinking and backlinking domains. We demonstrate the success of graph neural networks in detecting news site reliability using these attributed webgraphs, and show that our baseline news site reliability classifier outperforms current SoTA methods on the PoliticalNews dataset, achieving an F1 score of 0.96. Finally, we introduce and evaluate a novel graph-based algorithm for discovering previously unknown misinformation news sources.

To influence the information landscape preceding and during the military invasion of Ukraine in February 2022, Russia initiated a disinformation campaign portraying Ukraine as a Nazi state. This study aims to compare discussions related to this campaign on Twitter and Telegram. The analysis reveals that the Nazis and Ukraine narrative was constant on Twitter but only emerged on Telegram after the invasion in channels that had previously focused on a broader set of conspiracy theories. Beyond the examination of Russian disinformation in this case study, the paper introduces an innovative methodology for constructing topic networks from social media data. This approach expands upon traditional topic modeling by incorporating the network properties of social media data to establish directed networks that characterize the interplay between conversation topics. Through this methodology, we gain the ability to observe topical evolutions, providing fresh insights into the disinformation campaign and its efficacy in shaping discussions around the Russian invasion on social media.

Among popular social media platforms, Reddit stands out for its decentralized approach to moderation and community management. Due to this and its community-based network structure, Reddit provides a unique environment for studying the diffusion of knowledge and beliefs over social media. While assortativity, polarization, and user behavior have been examined within empirical contexts, having the ability to model the impacts of different moderation policies and rules across communities could provide useful insights for limiting the spread of misinformation. In this work, we introduce an agent-based model of Reddit interactions and moderating actions. By simulating interactions at the user level and specifying user-specific attributes, our model allows practitioners to conduct experiments with various types of actors and moderators and study their potential impact on Reddit-facilitated discussions and information diffusion. Additionally, subreddit-specific attributes enable communities to have different standards and thresholds for user conduct. To validate this model, we rely on an empirical dataset of over 100K posts and 800K comments across three U.S. political events in addition to user surveys and studies.

Online social connections occur within a specific conversational context. Prior work in network analysis of social media data attempts to contextualize data through filtering. We propose a method of contextualizing online conversational connections automatically and illustrate this method with Twitter data. Specifically, we detail a graph neural network model capable of representing tweets in a vector space based on their text, hashtags, URLs, and neighboring tweets. Once tweets are represented, clusters of tweets uncover conversational contexts. We apply our method to a dataset with 4.5 million tweets discussing the 2020 US election. We find that even filtered data contains many different conversational contexts, with users engaging in multiple conversations. While users engage in multiple conversations, the overlap between any two pairs of conversations tends to be only 30-40%, giving very different networks for different conversations. Even accounting for this variation, we show that the relative social status of users varies considerably across contexts, with tau=0.472 on average. Our findings imply that standard network analysis on social media data can be unreliable in the face of multiple conversational contexts.

Despite rapid development, current bot detection models still face challenges in dealing with incomplete data and cross-platform applications. In this paper, we propose BotBuster, a social bot detector built with the concept of a mixture of experts approach. Each expert is trained to analyze a portion of account information, e.g. username, and are combined to estimate the probability that the account is a bot. Experiments on 10 Twitter datasets show that BotBuster outperforms popular bot-detection baselines (avg F1=73.54 vs avg F1=45.12). This is accompanied with F1=60.04 on a Reddit dataset and F1=60.92 on an external evaluation set. Further analysis shows that only 36 posts is required for a stable bot classification. Investigation shows that bot post features have changed across the years and can be difficult to differentiate from human features, making bot detection a difficult and ongoing problem.

Target-stance detection on large-scale datasets is a core component of many of the most common stance detection applications. However, despite progress in recent years, stance detection research primarily occurs at the document-level on small-scale data. We propose a highly efficient Twitter Stance Propagation Algorithm (TSPA) for detecting user-level stance on Twitter that leverages the social networks of Twitter users and runs in near-linear time. We find TSPA achieves SoTA accuracy against BERT, homogenous Graph Attention Networks (GAT), and heterogenous GAT baselines. Additionally, TSPA's wall-clock time was 10x faster than our best baseline on a GPU and over 100x faster than our best baseline on a CPU.

This case study investigates a recent Russian disinformation narrative about U.S. biolabs and the development of biological weapons in Ukraine. This disinformation campaign was officially initiated by the Russian government, including the Russian Ministry of Defense, and was disseminated by official state-funded Russian media. In their announcement, Russian officials condemned pharmaceutical companies such as Pfizer and Moderna, as well as the U.S. military, for developing biological weapons in Ukraine. Four datasets with tweets were collected in English and Russian to analyze narratives and communities involved in the Ukrainian biolabs conversations on Twitter.This study aims to investigate how this disinformation theme was propagated in two languages on Twitter. Twitter social media data analysis, network influence metrics, coordination analysis, Leiden clustering, and qualitative data analysis were used in this study. The results have demonstrated that this particular disinformation campaign targeted both English and Russian-speaking audiences with coordinated inauthentic amplification to spread this disinformation to support Russia’s invasion of Ukraine. However, we found that this narrative was also naturally propagated by anti-vax and conservative communities among English-speaking users on Twitter.

Stance detection identifies a person’s evaluation of a subject, and is a crucial component for many downstream applications. In application, stance detection requires training a machine learning model on an annotated dataset and applying the model on another to predict stances of text snippets. This cross-dataset model generalization poses three central questions, which we investigate using stance classification models on 7 publicly available English Twitter datasets ranging from 297 to 48,284 instances. (1) Are stance classification models generalizable across datasets? We construct a single dataset model to train/test dataset-against-dataset, finding models do not generalize well (avg F1=0.33). (2) Can we improve the generalizability by aggregating datasets? We find a multi dataset model built on the aggregation of datasets has an improved performance (avg F1=0.69). (3) Given a model built on multiple datasets, how much additional data is required to fine-tune it? We find it challenging to ascertain a minimum number of data points due to the lack of pattern in performance. Investigating possible reasons for the choppy model performance we find that texts are not easily differentiable by stances, nor are annotations consistent within and across datasets. Our observations emphasize the need for an aggregated dataset as well as consistent labels for the generalizability of models.

This paper posits and tests a social cybersecurity framework to detect and characterize online trolling. Using a dataset of online trolling obtained through active learning, we empirically find that troll messages are significantly associated with more abusive language (p<.001), lower cognitive complexity (p<.01), and greater targeting of named entities (p<.05) and identities (p<.05). These effects are robust to the likelihood that these messages come from bots. We then train and evaluate TrollHunter, a theory-driven and interpretable machine learning model using the derived psycholinguistic features. TrollHunter achieves 89% accuracy and F1 score in detecting trolling messages, with an average 12.25% improvement in performance when relationally modeling conversational context. Explorations of convergent and discriminant validity reveal that our measure of trolling is more closely related to non-hateful offensive speech over hate speech, aggressive over non-aggressive speech, and that Chinese state-sponsored accounts engage in higher levels of trolling than Russian state-sponsored accounts (p<.001). Finally, we apply TrollHunter in a field study to compare the media targets of trolling activity compared to bots as a reference group. Bots dominate replies to exclusive right-leaning media outlets like Breitbart and Newsmax, while trolls disproportionately target outlets with mixed partisan trust like BBC and ABC. This bifurcation suggests that not only are trolls and bots different entities, but they also have different impacts in relation to driving polarization and disinformation in society. Echoing recent calls for interdisciplinary approaches that link computational models with social theory, we conclude with implications for platform regulation and policy-making to curtail the actions of diverse agents of disinformation.

Organizational risk and resilience as well as insider threat have been studied through the lenses of socio-psychological studies and information and computer sciences. As with all disciplines, it is an area in which practitioners, enthusiasts, and experts discuss the theory, issues, and solutions of the field in various online public forums. Such conversations, despite their public nature, can be difficult to understand and to study, even by those deeply involved in the communities themselves. Who are the key actors? How can we understand and characterize the culture around such communities, the problems they face, and the solutions favored by the experts in the field? Which narratives are being created and propagated, and by whom - and are these actors truly people, or are they autonomous agents, or "bots"?

Discussion about the interference of Russian actors in the 2016 U.S. presidential election campaign attracted enormous attention from the academic community. Numerous studies dedicated to the analysis of Internet operations, as well as activities of bots and trolls, formed a new interdisciplinary area that investigates online disinformation and computational propaganda. This study provides an analysis of a case study with Russian propaganda operations that focus on the internal political confrontation between the Russian systemic political establishment and opposition movement of Alexei Navalny. We present an analysis of how Internet trolls and sockpuppets are used to conduct information disorder activities in order to frame the discussion around the opposition movement in Russia on Twitter. We also identified attempts to manipulate the opinion of the Western audience and to spread disinformation about Western democracies by the same malicious actors. The study implements network analysis for identifying disinformation and propaganda trolls.Preliminary findings demonstrate that there is evidence of information campaigns against Alexei Navalny as one of the leaders of the Russian opposition. We observe how an internal issue is framed in the context of Russian confrontation with the West and how it is used to promote hostile narratives with the claims that Alexei Navalny is supported by the Western governments and therefore is an enemy of the Russian state. Many agents from our sample pretend to be real people, English speakers, who exhibit hostile attitudes towards Navalny and the Western democracies, promoting a lack of trust in the democratic institutions as well as spreading disinformation and conspiracy theories.

Online social media has become an important platform to organize around different socio-cultural and political topics. An extensive scholarship has discussed how people are divided into echo-chamber-like groups. However, there is a lack of work related to quantifying hostile communication or affective polarization between two competing groups. This paper proposes a systematic, network-based methodology for examining affective polarization in online conversations. Further, we apply our framework to 100 weeks of Twitter discourse about climate change. We find that deniers of climate change (Disbelievers) are more hostile towards people who believe (Believers) in the anthropogenic cause of climate change than vice versa. Moreover, Disbelievers use more words and hashtags related to natural disasters during more hostile weeks as compared to Believers. These findings bear implications for studying affective polarization in online discourse, especially concerning the subject of climate change. Lastly, we discuss our findings in the context of increasingly important climate change communication research.

Geotagging on social media has become an important proxy for understanding people's mobility and social events. Research that uses geotags to infer public opinions relies on several key assumptions about the behavior of geotagged and non-geotagged users. However, these assumptions have not been fully validated. Lack of understanding the geotagging behavior prohibits people further utilizing it. In this paper, we present an empirical study of geotagging behavior on Twitter based on more than 40 billion tweets collected from 20 million users. There are three main findings that may challenge these common assumptions. Firstly, different groups of users have different geotagging preferences. For example, less than 3% of users speaking in Korean are geotagged, while more than 40% of users speaking in Indonesian use geotags. Secondly, users who report their locations in profiles are more likely to use geotags, which may affects the generability of those location prediction systems on non-geotagged users. Thirdly, strong homophily effect exists in users' geotagging behavior, that users tend to connect to friends with similar geotagging preferences.

We use Twitter streaming API for many purposes like monitoring brands and discovering events. Because Twitter Streaming API only allows tracking words (commonly called 'search-terms'), the data collection goal needs to be formulated in terms of search terms. Twitter limits the number of search terms that can be tracked using the API, and the number of tweets retrieved per search-term depends on the terms being tracked. Therefore it's crucial to use a small set of highly relevant terms for tracking.

Automated social media bots have existed almost as long as the social media platforms they inhabit. Although efforts have long existed to detect and characterize these autonomous agents, these efforts have redoubled in the recent months following sophisticated deployment of bots by state and non-state actors. This research will study the differences between human and bot social communication networks by conducting an account snow ball data collection, and then evaluate features derived from this communication network in several bot detection machine learning models.

A mobility event occurs when a passenger moves out or takes off from a particular location. Mobility event prediction is of utmost importance in the field of intelligent transportation systems. It has a huge potential in solving important problems such as minimizing passenger waiting time and maximizing the utilization of the transportation resources by planning vehicle routes and dispatching transportation resources. Recently, numerous mobility pattern mining methods have been proposed to predict the transportation supply and demand in different locations. Those methods first reveal the event patterns of each Place of Interests (POI) independently and then employ a separate region function as a post-processing step. This separate process, that disregards the intrinsic spatial and temporal pattern correlations between POI, is sub-optimal and complex, resulting in a poor generalization in different scenarios. In this work, we propose a Spatial-Temporal mobility Event Prediction framework based on Deep neural network (StepDeep) for simultaneously taking into account all correlated spatial and temporal mobility patterns. StepDeep not only simplifies the prediction process but also enhances the prediction accuracy. Our StepDeep proposes a novel problem formulation towards an end-to-end mobility prediction framework, that is, switching mobility events over time in an area into an event video and then posing the mobility prediction problem as a video prediction task. Such a novel formulation can naturally encode spatial and temporal dependencies for each POI. StepDeep thus predicts the spatial-temporal events by incorporating the new time sensitive convolution filters, spatial sensitive convolution filters, and spatial-temporal sensitive convolution filters into a single network. We conduct experimental evaluations on a real-world 547-day New York City taxi trajectory dataset, which show that StepDeep provides higher prediction accuracy than five existing baselines. Moreover, StepDeep is generalizable and can be applied to numerous spatial-temporal event prediction scenarios.

The study of information flow typically does not distinguish the choices of tie strength on which the information flows. All receivers of the information are assumed to have the same potential to pass on the information. Modifying the SEIZ (susceptible, exposed, infected, skeptic) model, we discover that people choose to retweet strong or weak ties based on the topic. We made two modifications in the model. In the first modification (Model I), we assume that the contact rates of agents in different compartment and the probability of an agent transitioning from one compartment to another are different for strong ties and weak ties. In the second modification (Model II), we assume that only the probability of transitioning is different for strong ties and weak ties. We discover that people do not discriminate strong ties and weak ties when retweeting controversial topic, perhaps because this topic can both be personal and breaking news. On the other hand, people discriminate strong ties and weak ties when retweeting non-controversial topic. They prefer to retweet strong ties when the topic is donation, and kids, and weak ties when the topic is news on hurricane and music. Meanwhile, SEIZ model and its modifications are found to be inadequate to model tweets on event promotion.

Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection. While tweet text is the most commonly used feature in location estimation, most of the prior works suffer from either the noise or the sparsity of textual features. In this paper, we aim to tackle these two problems. We use topic modeling as a building block to characterize the geographic topic variation and lexical variation so that "one-hot" encoding vectors will no longer be directly used. We also incorporate other features which can be extracted through the Twitter streaming API to overcome the noise problem. Experimental results show that our RATE algorithm outperforms several benchmark methods, both in the precision of region classification and the mean distance error of latitude and longitude regression.

Due to the rapid increase in the number of users owning location-based devices, there is a considerable amount of geo-tagged data available on social media websites, such as Twitter and Facebook. This geo-tagged data can be useful in a variety of ways to extract location-specific information, as well as to comprehend the variation of information across different geographical regions. A lot of techniques have been proposed for extracting location-based information from social media, but none of these techniques aim to utilize an important characteristic of this data, which is the presence of aspects and their opinions, expressed by the users on these platforms. In this paper, we propose Geographic Aspect Opinion model (GASPOP), a probabilistic model that jointly discovers the variation of aspect and opinion, that correspond to different topics across various geographical regions from geo-tagged social media data. It incorporates the syntactic features of text in the generative process to differentiate aspect and opinion words from general background words. The user-based modeling of topics, also enables it to determine the interest distribution of various users. Furthermore, our model can be used to predict the location of different tweets based on their text. We evaluated our model on Twitter data, and our experimental results show that GASPOP can jointly discover latent aspect and opinion words for different topics across latent geographical regions. Moreover, a quantitative analysis of GASPOP using widely used evaluation metrics shows that it outperforms the state-of-the-art methods.

The presence of bots has been felt in many aspects of social media. Twitter, one example of social media, has especially felt the impact, with bots accounting for a large portion of its users. These bots have been used for malicious tasks such as spreading false information about political candidates and inflating the perceived popularity of celebrities. Furthermore, these bots can change the results of common analyses performed on social media. It is important that researchers and practitioners have tools in their arsenal to remove them. Approaches exist to remove bots, however they focus on precision to evaluate their model at the cost of recall. This means that while these approaches are almost always correct in the bots they delete, they ultimately delete very few, thus many bots remain. We propose a model which increases the recall in detecting bots, allowing a researcher to delete more bots. We evaluate our model on two real-world social media datasets and show that our detection algorithm removes more bots from a dataset than current approaches.

Sociologists have long been interested in the ways that identities, or labels for people, are created, used and applied across various social contexts. The present work makes two contributions to the study of identity, in particular the study of identity in text. We first consider the following novel NLP task: given a set of text data (here, from Twitter), label each word in the text as being representative of a (possibly multi-word) identity. To address this task, we develop a comprehensive feature set that leverages several avenues of recent NLP work on Twitter and use these features to train a supervised classifier. Our model outperforms a surprisingly strong rule-based baseline by 33%. We then use our model for a case study, applying it to a large corpora of Twitter data from users who actively discussed the Eric Garner and Michael Brown cases. Among other findings, we observe that the identities used by individuals differ in interesting ways based on social context measures derived from census data.

Social media is rapidly becoming one of the mediums of choice for understanding the cultural pulse of a region; e.g., for identifying what the population is concerned with and what kind of help is needed in a crisis. To assess this cultural pulse it is critical to have an accurate assessment of who is saying what in social media. However, social media is also the home of malicious users engaged in disruptive, disingenuous, and potentially illegal activity. A range of users, both human and non-human, carry out such social cyber-attacks. We ask, to what extent does the presence or absence of such users influence our ability to assess the cultural pulse of a region? We conduct a series of experiments to analyze the fragility of social network assessments based on Twitter data by comparing changes in both the structural and content results when suspended users are left in and taken out. Because a Twitter account can be suspended for various reasons including spamming or spreading ideas that can lead to extremism or terrorism, we separately assess the impacts of removing apparent spam bots and apparent extremists. Experimental results demonstrate that Twitter-based network structures and content are unstable, and can be highly impacted by the removal of suspended users. Further, the results exhibit regional and temporal variation that may be related to the political situation or civil unrest. We also provides guidance on the differential impact of different types of potentially suspendable users.

Given social networks over time, how can we measure network activities across different timesteps with a limited number of metrics? We propose two classes of dynamic metrics for assessing temporal evolution patterns of agents in terms of persistency and emergence. For each class of dynamic metrics, we implement it using three different temporal aggregation models ranging from the most commonly used Average Aggregation Model to more the complex models such as the Exponential Aggregation Model. We argue that the problem of measuring temporal patterns can be formulated using Recency and Primacy effect, which is a concept used to characterize human cognitive processes. Experimental results show that the way metrics model Recency--Primacy effect is closely related to their abilities to measure temporal patterns. Furthermore, our results indicate that future network agent activities can be predicted based on history information using dynamic metrics. By conducting multiple experiments, we are also able to find an optimal length of history information that is most relevant to future activities. This optimal length is highly consistent within a dataset and can be used as an intrinsic metric to evaluate a dynamic social network.

Peter Blau was one of the first to define a latent social space and utilize it to provide concrete hypotheses. Blau defines social structure via social “parameters” (constraints). Actors that are closer together (more homogenous) in this social parameter space are more likely to interact. One of Blau’s most important hypotheses resulting from this work was that the consolidation of parameters could lead to isolated social groups. For example, the consolidation of race and income might lead to segregation. In the present work, we use Foursquare data from New York City to explore evidence of homogeneity along certain social parameters and consolidation that breeds social isolation in communities of locations checked in to by similar users. More specifically, we first test the extent to which communities detected via Latent Dirichlet Allocation are homogenous across a set of four social constraints—racial homophily, income homophily, personal interest homophily and physical space. Using a bootstrapping approach, we find that 14 (of 20) communities are statistically, and all but one qualitatively, homogenous along one of these social constraints, showing the relevance of Blau’s latent space model in venue communities determined via user check-in behavior. We then consider the extent to which communities with consolidated parameters, those homogenous on more than one parameter, represent socially isolated populations. We find communities homogenous on multiple parameters, including a homosexual community and a “hipster” community, that show support for Blau’s hypothesis that consolidation breeds social isolation. We consider these results in the context of mediated communication, in particular in the context of self-representation on social media.

When a crisis occurs, there is often little time to evaluate the situation and determine how best to respond. We use rapid ethnographic methods centered on the construction of geo-temporally contextualized social and knowledge networks. By utilizing a combination of Twitter and news media, the consulate attack in Libya were examined in near real time. In this work we outline a procedure to extract key insights from the event as an event unfolds using a suite of tools developed by a team of researchers from two universities.

Automation of data collection using online resources has led to significant changes in traditional practices of social network analysis. Social network analysis has been an active research field for many decades; however, most of the early work employed very small datasets. In this paper, a number of issues with traditional practices of social network analysis in the context of dynamic, large-scale social networks are pointed out. Given the continuously evolving nature of modern online social networking, we postulate that social network analysis solutions based on incremental algorithms will become more important to address high computation times for large, streaming, over-time datasets. Incremental algorithms can benefit from early pruning by updating the affected parts only when an incremental update is made in the network. This paper provides an example of this case by demonstrating the design of an incremental closeness centrality algorithm that supports efficient computation of all-pairs of shortest paths and closeness centrality in dynamic social networks that are continuously updated by addition, removal, and modification of nodes and edges. Our results obtained on various synthetic and real-life datasets provide significant speedups over the most commonly used method of computing closeness centrality, suggesting that incremental algorithm design is a fruitful research area for social network analysts.

The increasing availability of dynamically growing digital data that can be used for extracting social networks has led to an upsurge of interest in the analysis of dynamic social networks. One key aspect of social network analysis is to understand the central nodes in a network. However, dynamic calculation of centrality values for rapidly growing networks might be unfeasibly expensive, especially if it involves recalculation from scratch for each time period. This paper proposes an incremental algorithm that effectively updates betweenness centralities of nodes in dynamic social networks while avoiding re-computations by exploiting information from earlier computations. Our performance results suggest that our incremental betweenness algorithm can achieve substantial performance speedup, on the order of thousands of times, over the state of the art, including the best-performing non-incremental betweenness algorithm and a recently proposed betweenness update algorithm.

The capability to build nuclear weapons is a key national security factor that has a profound influence on the balance of international relations. In addition to longstanding players, regional powers and peripheral countries have sought for ways of acquiring and/or developing them. The authors postulate that to express the capabilities, relative positions, and interrelations of the countries involved in the production of nuclear weaponization knowledge, dynamic network analysis provides valuable insight. In this article, the authors use a computational framework that combines techniques from dynamic network analysis and text mining to mine and analyze large‐scale networks that are extracted from open theoretical and experimental nuclear research publications of the last two decades. More specifically, they build interlinked, dynamic networks that model relationships of nuclear researchers based on the open literature and supplement this information with text mining to classify the nuclear weaponization capabilities of each publication—of each author, organization, city, and country. Using such a comprehensive computational framework, they are able to (a) elicit the hot topics in nuclear weaponization research, (b) assess the nuclear expertise level of each country, (c) differentiate between established and emergent players, and (d) identify the key entities at various levels such as organization, city, and country.

Abstract This research draws on longitudinal network data from an online community to examine patterns of users' behavior and social interaction, and infer the processes underpinning dynamics of system use. The online community represents a prototypical example of a complex evolving social network in which connections between users are established over time by online messages. We study the evolution of a variety of properties since the inception of the system, including how users create, reciprocate, and deepen relationships with one another, variations in users' gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the system. Results indicate that the system is a “small world” characterized by the emergence, in its early stages, of a hub‐dominated structure with heterogeneity in users' behavior. We investigate whether hubs are responsible for holding the system together and facilitating information flow, examine first‐mover advantages underpinning users' ability to rise to system prominence, and uncover gender differences in users' gregariousness, popularity, and local redundancy. We discuss the implications of the results for research on system use and evolving social networks, and for a host of applications, including information diffusion, communities of practice, and the security and robustness of information systems.