In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.

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Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions

Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. We consider information cascades in the context of recommendations, and in particular study the patterns of cascading recommendations that arise in large social networks. We investigate a large person-to-person recommendation network, consisting of four million people who made sixteen million recommendations on half a million products. Such a dataset allows us to pose a number of fundamental questions: What kinds of cascades arise frequently in real life? What features distinguish them? We enumerate and count cascade subgraphs on large directed graphs; as one component of this, we develop a novel efficient heuristic based on graph isomorphism testing that scales to large datasets. We discover novel patterns: the distribution of cascade sizes is approximately heavy-tailed; cascades tend to be shallow, but occasional large bursts of propagation can occur. The relative abundance of different cascade subgraphs suggests subtle properties of the underlying social network and recommendation process.

Patterns of influence in a recommendation network

Diffusion channels are critical to determining the adoption scale, which leads to the ultimate impact of an innovation. The aim of this study is to develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs. virality) on innovation adoption. Using citations of a series of classic algorithms and the time series of co‐authorship as the footprints of their diffusion trajectories, we propose a novel method to analyze the intertwining relationships between broadcasting and virality in the innovation diffusion process. Our findings show that broadcasting and virality have similar diffusion power, but play different roles across diffusion stages. Broadcasting is more powerful in the early stages but may be gradually caught up or even surpassed by virality in the later period. Meanwhile, diffusion speed in virality is significantly faster than broadcasting and members from virality channels tend to adopt the same innovation repetitively.

Innovation adoption: Broadcasting versus virality

Conspiracy theories proliferate online. We provide an overview of information consumption patterns related to conspiracy content on four mainstream social media platforms (Facebook, Twitter, YouTube, and Reddit), with a focus on niche ones. Opinion polarisation and echo chambers appear as pivotal elements of communication around conspiracy theories. A relevant role may also be played by the content moderation policies enforced by each social media platform. Banning contents or users from a social media could lead to a level of user segregation that goes beyond echo chambers and reaches the entire social media space, up to the formation of ‘echo platforms’. The insurgence of echo platforms is a new online phenomenon that needs to be investigated as it could foster many dangerous phenomena that we observe online, including the spreading of conspiracy theories. 

Conspiracy theories and social media platforms.

The paper’s main aim is to investigate and predict major factors in students’ behavioral intentions toward academic use of Facebook/Meta as a virtual classroom, taking into account its adoption level, purpose, and education usage. In contrast to earlier social network research, this one utilized a novel technique that comprised a two-phase analysis and an upcoming the Artificial Neural Network (ANN) analysis approach known as deep learning was engaged to sort out relatively significant predictors acquired from Structural Equation Modeling (SEM). This study has confirmed that perceived task-technology fit is the most affirmative and meaningful effect on Facebook/Meta usage in higher education. Moreover, facilitating conditions, collaboration, subjective norms, and perceived ease of use has strong influence on Facebook usage in higher education. The study’s findings can be utilized to improve the usage of social media tools for teaching and learning, such as Facebook/Meta. There is a discussion of both theoretical and practical implications. 

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Facebook/Meta usage in higher education: A deep learning-based dual-stage SEM-ANN analysis

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

/pdf/conspiracy-vs-science-a-large-scale-analysis-of-online-4lugqxk790.pdf

Conspiracy vs Science: A Large-scale Analysis of Online Discussion Cascades

Online product reviews through which consumers express their opinions and experiences with products are extremely valuable for both potential buyers to make informed purchase decisions and retailers to improve their products/services and adjust existing marketing strategies. One of the key challenges for mining product reviews is how to obtain a “ground truth” to guide the segmentation of reviewers properly. We propose a behavior-to-opinion approach, in which users are first categorized based on some unambiguous behavioral patterns (if available) and their online reviews are then classified to reveal unique and detailed characteristics of each user category. In this paper, we identify four categories of book consumers (i.e., kindle-only, print-only, print-to-kindle, and kindle-to-print) based on the long-term patterns of their review behavior. Their review posts are then clustered through word2vec and K-means, and four categories of adopters are matched with their concerned word topics. Finally, we find that print-only adopters show significantly different patterns on content-oriented topics as compared to other three groups. Kindle-to-print adopters pay more attention on portability whereas print-to-kindle adopters stress more on money and user experience. Taken together, our work indicates a diversity of characteristics among four categories of book reviewers.

Segmenting and Characterizing Adopters of E-Books and Paper Books Based on Amazon Book Reviews

Quantifying the virality of cascades is an important question across disciplines such as the transmission of disease, the spread of information and the diffusion of innovations. An appropriate virality metric should be able to disambiguate between a shallow, broadcast-like diffusion process and a deep, multi-generational branching process. Although several valuable works have been dedicated to this field, most of them fail to take the position of the diffusion source into consideration, which makes them fall into the trap of graph isomorphism and would result in imprecise estimation of cascade virality inevitably under certain circumstances. 
In this paper, we propose a root-aware approach to quantifying the virality of cascades with proper consideration of the root node in a diffusion tree. With applications on synthetic and empirical cascades, we show the properties and potential utility of the proposed virality measure. Based on preferential attachment mechanisms, we further introduce a model to mimic the growth of cascades. The proposed model enables the interpolation between broadcast and viral spreading during the growth of cascades. Through numerical simulations, we demonstrate the effectiveness of the proposed model in characterizing the virality of growing cascades. Our work contributes to the understanding of cascade virality and growth, and could offer practical implications in a range of policy domains including viral marketing, infectious disease and information diffusion.

Go viral or go broadcast? Characterizing the virality and growth of cascades.

Quantifying the virality of cascades is an important question across disciplines such as the transmission of disease, the spread of information and the diffusion of innovations. An appropriate virality metric should be able to disambiguate between a shallow, broadcast-like diffusion process and a deep, multi-generational branching process. Although several valuable works have been dedicated to this field, most of them fail to take the position of the diffusion source into consideration, which makes them fall into the trap of graph isomorphism and would result in imprecise estimation of cascade virality inevitably under certain circumstances. In this letter, we propose a root-aware approach to quantifying the virality of cascades with proper consideration of the root node in a diffusion tree. With applications on synthetic and empirical cascades, we show the properties and potential utility of the proposed virality measure. Based on preferential attachment mechanisms, we further introduce a model to mimic the growth of cascades. The proposed model enables the interpolation between broadcast and viral spreading during the growth of cascades. Through numerical simulations, we demonstrate the effectiveness of the proposed model in characterizing the virality of growing cascades. Our work contributes to the understanding of cascade virality and growth, and could offer practical implications in a range of policy domains including viral marketing, infectious disease and information diffusion.

Viral vs. broadcast: Characterizing the virality and growth of cascades

Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective outcomes. Leveraging a large-scale dataset from a knowledge-sharing website, this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social reputation. To capture the structural diversity of an individual, we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into consideration. We show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation, and the inclusion of a coexposure network provides an additional ingredient to achieve that goal. After synthetically controlling several possible confounding factors through matching experiments, structural diversity still plays a nonnegligible role in the prediction of personal online social reputation. Our work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains, such as social influence and collective intelligence studies.

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Yafei Zhang

Papers

Conspiracy vs Science: A Large-scale Analysis of Online Discussion Cascades

Segmenting and Characterizing Adopters of E-Books and Paper Books Based on Amazon Book Reviews

Go viral or go broadcast? Characterizing the virality and growth of cascades.

Viral vs. broadcast: Characterizing the virality and growth of cascades

The Strength of Structural Diversity in Online Social Networks.