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Positive and negative behavioral analysis in social networks: Positive and negative behavioral analysis
Abstract
Use of online social networks has grown dramatically since the first Web 2.0 technologies were deployed in the early 2000s. Our ability to capture user data, in particular behavioral data has grown in concert with increased use of these social systems. In this study, we survey methods for modeling and analyzing online user behavior. We focus on negative behaviors (social spamming and cyberbullying) and mitigation techniques for these behaviors. We also provide information on the interplay between privacy and deception in social networks and conclude by looking at trending and cascading models in social media. WIREs Data Mining Knowl Discov 2017, 7:e1203. doi: 10.1002/widm.1203
This article is categorized under: Commercial, Legal, and Ethical Issues > Social Considerations
... Squicciarini et al . [ 1 ], reviewed varying analytical methods that capture negative or antisocial behaviour more specifically social spamming and cyberbullying. Their study highlighted the ability of social networking sites to collect user data, including behaviour data, and they asserted negative behaviours can be prevented through using different techniques. ...
... Moreover, [ 1 ] suggests that one technique to analyse user behaviour is to use cascading models. A longitudinal study by Müller et al . ...
... Squicciarini et al . [ 1 ] reviewed analytical methods for identifying and preventing negative behaviours like social spamming and cyberbullying. Their work emphasized the role of social networking sites in collecting behaviour data and proposed different techniques for prevention. ...
Cyberbullying on social media is a significant public health concern. This paper systematically reviews the existing literature on cyberbullying to provide a clearer understanding of how it is defined and reported in terms of prevalence and impact. Utilizing the PRISMA search strategy, we examined 71 papers published from 2007 to 2022, offering a comprehensive synthesis of the field’s current understanding. Our findings highlight notable inconsistencies in the definition of cyberbullying across studies, underlining a critical need for a standardized conceptual framework. Additionally, while cyberbullying is shown to be highly prevalent among personalities exhibiting traits of Machiavellianism, psychopathy, and narcissism, our review identifies a crucial research gap: the underexploration of cyberbullying among adult populations. This review synthesizes the breadth of research on cyberbullying and highlights gaps in the existing literature. We have included our proposed standardized definition of cyberbullying.
... Squicciarini et al . [ 1 ], reviewed varying analytical methods that capture negative or antisocial behaviour more specifically social spamming and cyberbullying. Their study highlighted the ability of social networking sites to collect user data, including behaviour data, and they asserted negative behaviours can be prevented through using different techniques. ...
... Moreover, [ 1 ] suggests that one technique to analyse user behaviour is to use cascading models. A longitudinal study by Müller et al . ...
... Squicciarini et al . [ 1 ] reviewed analytical methods for identifying and preventing negative behaviours like social spamming and cyberbullying. Their work emphasized the role of social networking sites in collecting behaviour data and proposed different techniques for prevention. ...
... Taking such aspects into account while tracking the evolution of the network, would raise the level of reliability and efficiency of the tracking algorithm. One of these aspects involve the analysis of the impact of behaviours change on the roles of the nodes within the community, such may be used to predict the state of the community in the future as in Asur, Parthasarathy, and Ucar (2009), Lee et al. (2014), and Squicciarini, Rajtmajer, and Griffin (2017). Examples of these measures were (1) Stability Index, measures the likelihood of a node to stay in its community, (2) Sociability Index, corresponds to number of node's Join and Leave events across different communities, (3) Popularity Index, like a community is popular if its members Join exceeds Leave events of it, and (4) Influence Index, that measures the influence of node on other nodes to join or leave a community. ...
The rapid growth of social networks opens interesting research opportunities to make use of the
massive information exchanged in day-to-day communication. One of the active research issues
related to this aspect is the study of online community formation and evolution in dynamic
social networks. As community structure is usually ambiguous, then defining how it evolves over
time becomes a challenge in terms of tracking mechanism and evaluation method. In this study,
we review the online communities and their evolution tracking mechanisms and discuss the
main categories of approaches for tracking community evolution and how they work. We
analyse the different solutions proposed under each community evolution tracking category and
provide an assessment of their projected performance. Finally, a discussion of analysis insights
concerning community evolution and its influence is introduced.
Without sufficient intelligence, police response to crimes occurs in the form a reactive retort. This is even more so in the case of cyberspace policing, as digital platforms increase the complexities involved in the overall police incident response development. In this paper, we briefly introduce cybercrime and the necessities that police forces have to deal with. We argue that there is an urgent need for development and adoption of proactive and preventive techniques to identify and curb cyber and cyber-enabled crimes. We then present topic modelling as one of effective preventive techniques for predicting behaviours that can potentially be linked to cybercrime activities on social media.
Reflecting on the thousands of diverse research studies of social media representation and digital privacy, this article presents a comprehensive summary of online personal strategies. First, the evolution of academic concepts about digital identity and the online self is summarised. Then, the article investigates the key dynamics of personal strategies and control issues in detail with ideas, experiences, stories and metaphors taken from 60 qualitative interviews from Central and Eastern Europe and Southeast Asia. According to the key findings of this article, the universal patterns of online personal strategies follow mostly conscious decisions, resulting in users maintaining 70% control of their digital footprints. However, the remaining 30% of online activities are unconscious floating with digital dynamics and resulting in a wide range of non-expected consequences from identity theft to kidnapping. In summary, an intercultural and intergenerational model highlights the complexity and diversity of the studied field, providing a reference framework for future studies. The closing section presents a discussion of those findings of this study that are inconsistent with commonplace assumptions and conclusions present in the academic literature, promoting for study those subjects that still need to be extended or explored. Link: https://journals.sagepub.com/doi/10.1177/0165551519879702
Recent work on natural categories suggests a framework for conceptualizing people's knowledge about emotions. Categories of natural objects or events, including emotions, are formed as a result of repeated experiences and become organized around prototypes (Rosch, 1978); the interrelated set of emotion categories becomes organized within an abstract-to-concrete hierarchy. At the basic level of the emotion hierarchy one finds the handful of concepts (love, joy, anger, sadness, fear, and perhaps, surprise) most useful for making everyday distinctions among emotions, and these overlap substantially with the examples mentioned most readily when people are asked to name emotions (Fehr & Russell, 1984), with the emotions children learn to name first (Bretherton & Beeghly, 1982), and with what theorists have called basic or primary emotions. This article reports two studies, one exploring the hierarchical organization of emotion concepts and one specifying the prototypes, or scripts, of five basic emotions, and it shows how the prototype approach might be used in the future to investigate the processing of information about emotional events, cross-cultural differences in emotion concepts, and the development of emotion knowledge.
Twitter is an online social network (OSN) with approximately 650 million users. It has been fairly characterized as one of the most influential OSNs since it includes public figures, organizations, news media and official authorities. Twitter has an inherent simple philosophy with short messages, friendship relations, hashtags and support for media sharing such as photos and short videos. Popular hashtags that emerge from users’ activity are displayed prominently in the platform as Popular Trends. Unfortunately, the capabilities of the platform can be also abused and exploited for distributing illicit content or boosting false information, and the consequences of such actions can be really severe: one false tweet was enough for making the stock market crash for a short period of time in 2013. In this study, we make an experimental analysis on a large dataset containing 150 million tweets. We delve into the dynamics of the popular trends as well as other Twitter features in regard to deliberate misuse. We investigate traditional spam techniques as well as an obfuscated way of spam campaigns that exploit trending topics and hides malicious URLs within Google search result links. We implement a simple and lightweight classifier for indentifying spam users as well as spam tweets. Finally, we visualize these spam campaigns and investigate their inner properties.
This article reports new advancements in the theory of influence system evolution in small deliberative groups, and a novel set of empirical findings on such evolution. The theory elaborates the specification of the single-issue opinion dynamics of such groups, which has been the focus of theory development in the field of opinion dynamics, to include group dynamics that occur along a sequence of issues. The theory predicts an evolution of influence centralities along issue sequences based on elementary reflected appraisal mechanisms that modify influence network structure and flows of influence in the group. The new empirical findings, which are also reported in this article, present a remarkable suite of issue-sequence effects on influence network structure consistent with theoretical predictions.
With the increasing popularity of user-contributed sites, the phenomenon of “social pollution”, the presence of abusive posts has become increasingly prevalent. In this paper, we describe a novel approach to investigate negative behavior dynamics in online social networks as epidemic phenomena. We show that using hybrid automata, it is possible to explain the contagion of antinormative behavior in certain online commentaries. We present two variations of a finite-state machine model for time-varying epidemic dynamics, namely triggered state transition and iterative local regression, which differ with respect to accuracy and complexity.We validate the model with experiments over a dataset of 400,000 comments on 800 YouTube videos, classified by genre, and indicate how different epidemic patterns of behavior can be tied to specific interaction patterns among users.
Cyber Bullying, which often has a deeply negative impact on the victim, has grown as a serious issue among adolescents. To understand the phenomenon of cyber bullying, experts in social science have focused on personality, social relationships and psychological factors involving both the bully and the victim. Recently computer science researchers have also come up with automated methods to identify cyber bullying messages by identifying bullying-related keywords in cyber conversations. However, the accuracy of these textual feature based methods remains limited. In this work, we investigate whether analyzing social network features can improve the accuracy of cyber bullying detection. By analyzing the social network structure between users and deriving features such as number of friends, network embeddedness, and relationship centrality, we find that the detection of cyber bullying can be significantly improved by integrating the textual features with social network features.
Community-based question answering platforms can be rich sources of
information on a variety of specialized topics, from finance to cooking. The
usefulness of such platforms depends heavily on user contributions (questions
and answers), but also on respecting the community rules. As a crowd-sourced
service, such platforms rely on their users for monitoring and flagging content
that violates community rules.
Common wisdom is to eliminate the users who receive many flags. Our analysis
of a year of traces from a mature Q&A site shows that the number of flags does
not tell the full story: on one hand, users with many flags may still
contribute positively to the community. On the other hand, users who never get
flagged are found to violate community rules and get their accounts suspended.
This analysis, however, also shows that abusive users are betrayed by their
network properties: we find strong evidence of homophilous behavior and use
this finding to detect abusive users who go under the community radar. Based on
our empirical observations, we build a classifier that is able to detect
abusive users with an accuracy as high as 83%.
Online dating sites are experiencing a rise in popularity, with one in five relationships in the United States starting on one of these sites. Online dating sites provide a valuable platform not only for single people trying to meet a life partner, but also for cybercriminals, who see in people looking for love easy victims for scams. Such scams span from schemes similar to traditional advertisement of illicit services or goods (i.e., spam) to advanced schemes, in which the victim starts a long-distance relationship with the scammer and is eventually extorted money.
In this paper we perform the first large-scale study of online dating scams. We analyze the scam accounts detected on a popular online dating site over a period of eleven months, and provide a taxonomy of the different types of scammers that are active in the online dating landscape. We show that different types of scammers target a different demographics on the site, and therefore set up accounts with different characteristics. Our results shed light on the threats associated to online dating scams, and can help researchers and practitioners in developing effective countermeasures to fight them.
With the growth of Web 2.0, online communication and social networks are emerging. This alternation helps users to share their information and collaborate with each other easily. In addition, these internet services help establish new connections between persons or reinforce existing ones. However, they can also lead to misbehaviors or cyber criminal acts for example, cyberbullying. At the same time, it can make children and adolescents to use the technologies for the intention of harming another person. Due to the negative effect of cyberbullying, some techniques and methods are proposed to overcome this problem. This paper illustrates a survey covering some methods and challenges in cyberbullying. Next, we offer suggestions for continued research in this area.
Significance
We show, via a massive ( N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. We provide experimental evidence that emotional contagion occurs without direct interaction between people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues.
Bullying has long been a concern of youth advocates (e.g., educators, counselors, researchers, policy makers). Recently, cyberbullying (bullying perpetrated through online technology) has dominated the headlines as a major current-day adolescent challenge. This article reviews available empirical research to examine the accuracy of commonly-perpetuated claims about cyberbullying. The analysis revealed several myths about the nature and extent of cyberbullying that are being fueled by media headlines and unsubstantiated public declarations. These myths include that (a) everyone knows what cyberbullying is; (b) cyberbullying is occurring at epidemic levels; (c) cyberbullying causes suicide; (d) cyberbullying occurs more often now than traditional bullying; (e) like traditional bullying, cyberbullying is a rite of passage; (f) cyberbullies are outcasts or just mean kids; and (g) to stop cyberbullying, just turn off your computer or cell phone. These assertions are clarified using data that are currently available so that adults who work with youth will have an accurate understanding of cyberbullying to better assist them in effective prevention and response. Implications for prevention efforts in education in light of these revelations are also discussed and include effective school policies, educating students and stakeholders, the role of peer helper programs, and responsive services (e.g., counseling).
One of the main goals of all online social communities is to promote a stable, or perhaps, growing membership built around topics of like interest. Yet, communities are not impermeable to the potentially damaging effects resulting from those few participants that choose to behave in a manner that is counter to established norms of behavior. Typical moderators in online social communities are the ones tasked to reduce the risks associated with unhealthy user behavior by rapidly identifying and removing damaging posts and consequently taking action against the perpetrating user. Yet, the sheer volume of posts relative to the number of moderators available for review suggests a need for modern tools aimed at prioritizing posts based on the assessed risk each user poses to the community. To accomplish this, we propose a threat analysis model. Our model, referred to as TrICO (Threat requires Intent Capability and Opportunity) is implemented using Bayesian Networks, and achieves early detection of damaging behavior in online social communities. To the best of our knowledge, this is the first user-centered model for usage policy enforcement in online sites. We apply our model to a comprehensive data set characterizing the entirety of a popular discussion forum. Our results show that the TrICO model provides accurate results.
Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.
This article draws on sociological and psychological theory to explore the meaning application of deviance to behaviors observed on the Internet. First, definitions of deviancein online and offline contexts are discussed. Observations of the Internet as a so-called yet-to-be-normalized environment present a conflicting scenario for labeling emergent behaviors as deviant. The question stands as to whether devianceis an appropriate term to apply to some behavior observed on the Internet. The second section examines deviance on the Internet at a macro, cybercultural level and at a micro, communicational level using two key examples to illustrate some of the issues raised earlier in defining deviance. The sharing of mp3 files is used as an example to illustrate problems in definition at a macro level and at a microlevel; psychological approximations to normative and antinormative communication on the Net are discussed, using flaming as an example.
The need for highly scalable and accurate detection and filtering of misbehaving users and obscene content in online video chat services has grown as the popularity of these services has exploded in popularity. This is a challenging problem because processing large amounts of video is compute intensive, decisions about whether a user is misbehaving or not must be made online and quickly, and moreover these video chats are characterized by low quality video, poorly lit scenes, diversity of users and their behaviors, diversity of the content, and typically short sessions. This paper presents EMeralD, a highly scalable system for accurately detecting and filtering misbehaving users in online video chat applications. EMeralD substantially improves upon the state-of-the-art filtering mechanisms by achieving much lower computational cost and higher accuracy. We demonstrate EMeralD's improvement via experimental evaluations on real-world data sets obtained from Chatroulette.com.
Social sites frequently ask for rich sets of user identity properties before granting access. Users are given the freedom to fail to respond to some of these requests, or can choose to submit fake identity properties, so as to reduce the risk of identification, surveillance or observation of any kind. However, this freedom has led to serious security and privacy incidents, due to the role users' identities play in establishing social and privacy settings. In this paper, we take a step toward addressing this open problem, by analyzing the dynamics of social identity verification protocols. Based on some real-world data, we develop a deception model for online users. The model takes a game theoretic approach to characterizing a user's willingness to release, withhold or lie about information depending on the behavior of individuals within the user's circle of friends. We provide an illustrative example and conjecture a relationship between the qualitative structure of Nash equilibria in the game and the auto orphism group of the social network.
Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. This feature makes the model particularly suited for the implementa- tion of classifiers and knowledge-based systems. When working with sets of (instead of single) probability distributions, the identification of the optimal option can be based on different criteria, some of them eventually leading to multiple choices. Yet, most of the inference algorithms for credal nets are designed to compute only the bounds of the posterior probabilities. This prevents some of the existing criteria from being used. To overcome this limitation, we present two simple transformations for credal nets which make it possible to compute decisions based on the maximality and E-admissibility criteria without any modification in the inference algorithms. We also prove that these decision problems have the same complexity of standard inference, being NP PP -hard for general credal nets and NP-hard for polytrees.
A wide variety of deviant behavior may arise as the population of an online multimedia community increases. That behavior can span the range from simple mischievous antics to more serious expressions of psychopathology, including depression, sociopathy, narcissism, dissociation, and borderline dynamics. In some cases the deviant behavior may be a process of pathological acting out - in others, a healthy attempt to work through. Several factors must be taken into consideration when explaining online deviance, such as social/cultural issues, the technical infrastructure of the environment, transference reactions, and the effects of the ambiguous, anonymous, and fantasy-driven atmosphere of cyberspace life. In what we may consider an "online community psychology," intervention strategies for deviant behavior can be explored along three dimensions: preventative versus remedial, user versus superuser based, and automated versus interpersonal.
The phenomenon of reputation has been extensively studied in economics, game theory in particular. The game theoretic framework for analysing reputation is that of repeated games in which some players are uncertain about the payofi structures of their opponents. There are two keyterms in this informal definition: repeated game and uncertainty. Both of them are well in accordance with informal understanding of the notion of reputation. The necessity of repetition is rather obvious-it makes sense to build reputation only if it can influence future returns. If all interactions are one time deals then reputation is not needed because no one will happen to know in the future what you did in the past. Uncertainty is just a little bit less obvious: one who is locked into specific type of behavior, without any other options to choose from, cannot have reputation for that specific type. Rather, he or she is of that type with certainty. In this text we present how game theory operationalizes this informal view on reputation and discuss how the developed models can be applied in open electronic communities to promote trustworthy behavior. Sections 2 and 3 lay out the ground for understanding complex reputation models by descibing ba-sic game-theoretic concepts including games with incomplete information and repeated games. Section 4 introduces game-theoretic reputation models on the example of the well known chain-store game, while Section 5 presents general-izations of the observations made on this example. These two sections deal with so called models with perfect monitoring in which the players are fully informed about the past plays of their opponents. Sections 6 and 7 discuss more recently developed models in which the players observe only imperfect signals of the past play. In Section 6 the signals are public, common for all players, while Section 7 deals with the case of privately observed, difierent, signals. We conclude in Sec-tion 8 with a discussion on possible ways to apply this game-theoretic modeling on online settings.
The description of large state spaces through stochastic structured modeling formalisms like stochastic Petri nets, stochastic automata networks and performance evaluation process algebra usually represent the infinitesimal generator of the underlying ...
A meta-analytic integration reviews evidence for deindividuation theory as an explanation of collective and antinormative behavior. Deindividuation theories propose a subjective deindividuated state that causes transgression of general social norms. Deindividuation research classically manipulates anonymity, self-awareness, and group size. Results of 60 independent studies showed little support for (a) the occurrence of deindividuated (antinormative) behaviors or (b) the existence of a deindividuated state. Research results were explained more adequately by situation-specific than by general social norms. Analyses indicated that groups and individuals conform more to situation-specific norms when they are "deindividuated." These findings are inconsistent with deindividuation theory but support a social identity model of deindividuation effects. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Predictions about the social causes of self-consciousness in groups were derived from the theory of deindividuation and tested in 3 experiments with 618 university students and adults. In Exp I, it was found that increasing group size was related to a decrease in self-consciousness. Group density did not influence self-consciousness. In Exp II, it was found that increases in the number of observers increased self-consciousness. In Exps I and II, self-reports of self-consciousness were independent of one's group, whereas the degree of behavioral disinhibition was highly correlated within groups. In Exp III, it was found that gender similarity within a group was related to lower self-consciousness. Findings support a perceptual/attentional model of self-consciousness within groups. Contrary to deindividuation theory predictions, however, behavior intensity did not vary across conditions in Exps I and II, even though self-consciousness did differ. This finding suggests that deindividuation theory is incomplete in its present form. (19 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
We present results of network analyses of information diffusion on Twitter, via users’ ongoing social interactions as denoted by “@username” mentions. Incorporating survival analysis, we constructed a novel model to capture the three major properties of information diffusion: speed, scale, and range. On the whole, we find that some properties of the tweets themselves predict greater information propagation but that properties of the users, the rate with which a user is mentioned historically in particular, are equal or stronger predictors. Implications for end users and system designers are discussed.
Bifurcation theory : an introduction with applications to PDEs. - New York u.a. : Springer, 2004. - VII, 346 S. - (Applied mathematical sciences ; 156)
Online Social Networks (OSNs) such as Facebook and Twitter have become popular communication and information sharing tools for hundreds of millions of individuals in recent years. OSNs not only make people’s life more connected, but also attract the interest of spammers. Twitter spam generally contains deceptive information, such as “free voucher” and “weight loss advertisement” to attract the interest of victims. A comprehensive analysis on the deceptive information will be of great benefit to the detection of Twitter spam. This paper presents a study of deceptive information in Twitter spam. The analysis is based on a collection of over 550 million tweets with around 6% spam. We find that various deceptive content of spam performs differently in luring victims to malicious sites. We also find the regional response rate to various Twitter spam outbreaks vary greatly. These two factors can contribute to improve the performance of spam detection techniques.
Online Social Networks (OSNs) have become more and more popular in the whole world. People share their personal activities, views and opinions among different OSNs. At the same time, social spam appears more frequently and in various formats throughout popular OSNs. Therefore, efficient detection of spam has become an important and popular problem. This paper focuses on spam detection across multiple online social networks by leveraging the knowledge of detecting similar spam within a social network and using it in different networks. We chose Facebook and Twitter for our study targets, considering that they share the most similar features in posts, topics, and user activities, etc. We collected two datasets from them and performed analysis based on our proposed methodology. The results show that detection combined with spam in Facebook show a more than 50% decrease of spam tweets in Twitter, and detection combined with spam of Twitter shows a nearly 71.2% decrease of spam posts in Facebook. This means similar spam of one social network can greatly facilitate spam detection in other social networks. We proposed a new perspective of spam detection in OSNs.
Cyberbullying is an increasingly prevalent phenomenon impacting young adults. In this paper, we present a study on both detecting cyberbullies in online social networks and identifying the pairwise interactions between users through which the influence of bullies seems to spread. In particular, we investigate the role of user demographics and social network features in predicting how users will respond to a cyberbullying comment. We characterize the influencer/influenced relationship by which a user who has no history of abuse observes a peer engaging in bullying and follows suit. To our knowledge, this is the first effort modeling peer pressure and social dynamics with analytical models. We validate our models on two distinct social network datasets, totalling over 16,000 posts. Our results offer insight into the dynamics of bullying and confirm social theories on the power of peer groups in the cyberworld. A full version of this paper is available on arXiv.org.
The privacy policies of an online social network play an important role in determining user involvement and satisfaction, and in turn site profit and success. In this paper, we develop a game theoretic framework to model the relationship between the set of privacy options offered by a social network site and the sharing decisions of its users within these constraints. We model the site and the users in this scenario as the leader and followers, respectively, in a Stackelberg game. We formally establish the conditions under which this game reaches a Nash equilibrium in pure strategies and provide an approximation algorithm for the site to determine a discrete set of privacy options to maximize payoff. We validate hypotheses in our model on data collected from a mock-social network of users’ privacy preferences both within and outside the context of peer influence, and demonstrate that the qualitative assumptions of our model are well-founded.
The advent of cloud computing and big data has radically altered the important role of innovation as a strategy for responding to the rapidly changing nature of an increasingly interconnected online world. This situation has necessitated revisions in the strategic concepts associated with sustaining long term performance by exploiting new technologies. Understanding of these new strategic approaches is provided by examining how the online world is being exploited by organisations in sectors of a modern economy such retailing, service firms, healthcare and the public sector in terms of creating new forms of competitive advantage within both pure play and terrestrial environments as a consequence of the advent of mobile technology and online social networks.
Predictions about the social causes of self-consciousness in groups were derived from the theory of deindividuation and tested in 3 experiments with 618 university students and adults. In Exp I, it was found that increasing group size was related to a decrease in self-consciousness. Group density did not influence self-consciousness. In Exp II, it was found that increases in the number of observers increased self-consciousness. In Exps I and II, self-reports of self-consciousness were independent of one's group, whereas the degree of behavioral disinhibition was highly correlated within groups. In Exp III, it was found that gender similarity within a group was related to lower self-consciousness. Findings support a perceptual/attentional model of self-consciousness within groups. Contrary to deindividuation theory predictions, however, behavior intensity did not vary across conditions in Exps I and II, even though self-consciousness did differ. This finding suggests that deindividuation theory is incomplete in its present form. (19 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).
Recent work has studied Twitter's role in distributing information about specific events, in acting as a platform for political debate, and in facilitating social interaction. Despite this interesting body of work, to our knowledge, it is unclear how trending words are used in Twitter, and what is their lifecycle. In this work, we investigate statistical models of the dynamics of word/phrase use in Twitter over time. We identify four base behaviors, derived from the autocorrelation functions of the frequency of word/phrase use. We then observe drift among these base behaviors in our sampled word/phrases over multiple weeks. To the best of our knowledge, this is the first time a hybrid statistical model using Markov processes and ARIMA sub-models have been used to explain the occurrence of certain n-grams within the linguistic space of Twitter topics. The ultimate objective of this work is to develop a hierarchical model for the behavior of word/phrase occurrence within Twitter. The model supposes that words/phrase dynamics move from one regime to another as various exogenous forces act on the population of users. This paper takes the first steps in illustrating that these regimes exist and shows some of the dynamics of regime change.
The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic.
We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors.
Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network.
Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.
As social networking sites continue to proliferate, online deception is becoming a significant problem. Deceptive users are now not only lone wolves propagating hate messages and inappropriate content, but also are more frequently seemingly honest users choosing to deceive for selfish reasons. Their behavior negatively influences otherwise honest online community members, creating a snowball effect that damages entire online communities. In this paper, we study the phenomenon of deception and attempt to understand the dynamics of users’ deception, using a game-theoretic approach. We begin by formulating the decision process of a single user as a Markov chain with time-varying rewards. We then study the specific optimization problem a user may face in choosing to deceive when they are influenced by (1) their potential reward, (2) peer pressure and (3) their deception comfort level. We illustrate reasonable equilibria can be achieved under certain simplifying assumptions. We then investigate the inverse problem: given equilibria, we show how we can fit a model to the data and how this model exposes information about the social structure.
Part I. Introduction: 1. Group dynamics: structural social psychology 2. Formalization: attitude change in influence networks 3. Operationalization: constructs and measures 4. Assessing the model Part II. Influence Network Perspective on Small Groups: 5. Consensus formation and efficiency 6. The smallest group 7. Social comparison theory 8. Minority and majority factions 9. Choice shift and group polarization Part III. Linkages with Other Formal Theories: 10. Models of group decision making 11. Expectation states and affect control 12. Individuals in groups Epilogue Appendices.
In this work, we design a method for blog comment spam detection using the assumption that spam is any kind of uninformative content. To measure the "informativeness" of a set of blog comments, we construct a language and tokenization independent metric which we call content complexity, providing a normalized answer to the informal question "how much information does this text contain?" We leverage this metric to create a small set of features well-adjusted to comment spam detection by computing the content complexity over groupings of messages sharing the same author, the same sender IP, the same included links, etc.
We evaluate our method against an exact set of tens of millions of comments collected over a four months period and containing a variety of websites, including blogs and news sites. The data was provided to us with an initial spam labeling from an industry competitive source. Nevertheless the initial spam labeling had unknown performance characteristics. To train a logistic regression on this dataset using our features, we derive a simple mislabeling tolerant logistic regression algorithm based on expectation-maximization, which we show generally outperforms the plain version in precision-recall space.
By using a parsimonious hand-labeling strategy, we show that our method can operate at an arbitrary high precision level, and that it significantly dominates, both in terms of precision and recall, the original labeling, despite being trained on it alone.
The content complexity metric, the use of a noise-tolerant logistic regression and the evaluation methodology are thus the three central contributions with this work.
Online social networks, such as Twitter, have soared in popularity and in turn have become attractive targets of spam. In fact, spammers have evolved their strategies to stay ahead of Twitter's anti-spam measures in this short period of time. In this paper, we investigate the strategies Twitter spammers employ to reach relevant target audiences. Due to their targeted approaches to send spam, we see evidence of a large number of the spam accounts forming relationships with other Twitter users, thereby becoming deeply embedded in the social network.
We analyze nearly 20 million tweets from about 7 million Twitter accounts over a period of five days. We identify a set of 14,230 spam accounts that manage to live longer than the other 73% of other spam accounts in our data set. We characterize their behavior, types of tweets they use, and how they target their audience. We find that though spam campaigns changed little from a recent work by Thomas et al., spammer strategies evolved much in the same short time span, causing us to sometimes find contradictory spammer behavior from what was noted in Thomas et al.'s work. Specifically, we identify four major strategies used by 2/3rd of the spammers in our data. The most popular of these was one where spammers targeted their own followers. The availability of various kinds of services that help garner followers only increases the popularity of this strategy. The evolution in spammer strategies we observed in our work suggests that studies like ours should be undertaken frequently to keep up with spammer evolution.
Social networks are popular platforms for interaction, communication, and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, Web browsing, and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone. This leads to the question: “Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially enhanced applications?” In this article, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of “interaction graphs” to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the “small-world” properties present in their social graph counterparts. This means that these graphs have fewer “supernodes” with extremely high degree, and overall graph diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate several well-known social-based applications that rely on graph properties to infuse new functionality into Internet applications, including Reliable Email (RE), SybilGuard, and the weighted cascade influence maximization algorithm. The results reveal new insights into each of these systems, and confirm our hypothesis that to obtain realistic and accurate results, ongoing research on social network applications studies of social applications should use real indicators of user interactions in lieu of social graphs.
The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.
A fundamental question when studying underlying friendship and interaction graphs of Online Social Networks (OSNs) is how closely these graphs mirror real-world associations. The presence of phantom or fake profiles dilutes the integrity of this correspondence. This paper looks at the presence of phantom profiles in the context of social gaming, i.e., profiles created with the purpose of gaining a strategic advantage in social games. Through a measurement-based study of a Facebook gaming application that is known to attract phantom profiles, we show statistical differences among a subset of features associated with genuine and phantom profiles. We then use supervised learning to classify phantom profiles. Our work represents a first-step towards solving the more general problem of detecting fake/phantom identities in OSNs.
Many models for the spread of infectious diseases in populations have been analyzed mathematically and applied to specific diseases. Threshold theorems involving the basic reproduction number R0, the contact number σ, and the replacement number R are reviewed for the classic SIR epidemic and endemic models. Similar results with new expressions for R0 are obtained for MSEIR and SEIR endemic models with either continuous age or age groups. Values of R0 and σ are estimated for various diseases including measles in Niger and pertussis in the United States. Previous models with age structure, heterogeneity, and spatial structure are surveyed.
In this study, we examine the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon. To perform our analysis, we identify over 1.1 million accounts suspended by Twitter for disruptive activities over the course of seven months. In the process, we collect a dataset of 1.8 billion tweets, 80 million of which belong to spam accounts. We use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide-spread abuse of legitimate web services such as URL shorteners and free web hosting. We also identify an emerging marketplace of illegitimate programs operated by spammers that include Twitter account sellers, ad-based URL shorteners, and spam affiliate programs that help enable underground market diversification.
Our results show that 77% of spam accounts identified by Twitter are suspended within on day of their first tweet. Because of these pressures, less than 9% of accounts form social relationships with regular Twitter users. Instead, 17% of accounts rely on hijacking trends, while 52% of accounts use unsolicited mentions to reach an audience. In spite of daily account attrition, we show how five spam campaigns controlling 145 thousand accounts combined are able to persist for months at a time, with each campaign enacting a unique spamming strategy. Surprisingly, three of these campaigns send spam directing visitors to reputable store fronts, blurring the line regarding what constitutes spam on social networks.
"A group phenomenon which we have called de-individuation has been described and defined as a state of affairs in a group where members do not pay attention to other individuals
qua individuals, and, correspondingly, the members do not feel they are being singled out by others." The theory was advanced that this results in a reduction of inner restraints in the members and that, consequently, the members will be more free to indulge in behavior from which they are usually restrained. It was further hypothesized that this is satisfying and its occurrence would tend to increase the attractiveness of the group. The data from the study tend to confirm the theory. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Online social networks (OSNs) are popular collaboration and com-munication tools for millions of users and their friends. Unfortu-nately, in the wrong hands, they are also effective tools for execut-ing spam campaigns and spreading malware. Intuitively, a user is more likely to respond to a message from a Facebook friend than from a stranger, thus making social spam a more effective distribution mechanism than traditional email. In fact, existing ev-idence shows malicious entities are already attempting to compro-mise OSN account credentials to support these "high-return" spam campaigns. In this paper, we present an initial study to quantify and char-acterize spam campaigns launched using accounts on online social networks. We study a large anonymized dataset of asynchronous "wall" messages between Facebook users. We analyze all wall messages received by roughly 3.5 million Facebook users (more than 187 million messages in all), and use a set of automated tech-niques to detect and characterize coordinated spam campaigns. Our system detected roughly 200,000 malicious wall posts with embed-ded URLs, originating from more than 57,000 user accounts. We find that more than 70% of all malicious wall posts advertise phish-ing sites. We also study the characteristics of malicious accounts, and see that more than 97% are compromised accounts, rather than "fake" accounts created solely for the purpose of spamming. Fi-nally, we observe that, when adjusted to the local time of the sender, spamming dominates actual wall post activity in the early morning hours, when normal users are asleep.
Social network communities facilitate the sharing of identity information in a directed network. Compared with traditional methods for identity information disclosure, such as a campus directory, the social network community fosters a more subjective and holistic disclosure of identity information. In the following paper, the results of a quantitative analysis of identity information disclosure in social network communities, as well as subject opinions regarding identity protection and information disclosure are presented. Through comparative analysis, the need for further analysis of the value and jeopardy of identity information sharing in social network communities is identified.