Why Did They #Unfollow Me? Early Detection of Follower Loss on Twitter

Having more followers has become a norm in recent social media and micro-blogging communities. This battle has been taking shape from the early days of Twitter. Despite this strong competition for followers, many Twitter users are continuously losing their followers. This work addresses the problem of identifying the reasons behind the drop of followers of users in Twitter. As a first step, we extract various features by analyzing the content of the posts made by the Twitter users who lose followers consistently. We then leverage these features to early detect follower loss. We propose various models and yield an overall accuracy of 73% with high precision and recall. Our model outperforms baseline model by 19.67% (w.r.t accuracy), 33.8% (w.r.t precision) and 14.3% (w.r.t recall).Comment: 5 pages, 1 table, GROUP '1

Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction

Online Social Networks (OSNs) evolve through two pervasive behaviors: follow and unfollow, which respectively signify relationship creation and relationship dissolution. Researches on social network evolution mainly focus on the follow behavior, while the unfollow behavior has largely been ignored. Mining unfollow behavior is challenging because user's decision on unfollow is not only affected by the simple combination of user's attributes like informativeness and reciprocity, but also affected by the complex interaction among them. Meanwhile, prior datasets seldom contain sufficient records for inferring such complex interaction. To address these issues, we first construct a large-scale real-world Weibo dataset, which records detailed post content and relationship dynamics of 1.8 million Chinese users. Next, we define user's attributes as two categories: spatial attributes (e.g., social role of user) and temporal attributes (e.g., post content of user). Leveraging the constructed dataset, we systematically study how the interaction effects between user's spatial and temporal attributes contribute to the unfollow behavior. Afterwards, we propose a novel unified model with heterogeneous information (UMHI) for unfollow prediction. Specifically, our UMHI model: 1) captures user's spatial attributes through social network structure; 2) infers user's temporal attributes through user-posted content and unfollow history; and 3) models the interaction between spatial and temporal attributes by the nonlinear MLP layers. Comprehensive evaluations on the constructed dataset demonstrate that the proposed UMHI model outperforms baseline methods by 16.44% on average in terms of precision. In addition, factor analyses verify that both spatial attributes and temporal attributes are essential for mining unfollow behavior.Comment: 8 pages, 7 figures, Accepted by AAAI 202

#unfollow on Instagram Factors that have an impact on the decision to unfollow public figures

The social media platform Instagram allows users to subscribe to various people from their immediate circle of acquaintances or to follow public figures. Recent research has identified reasons concerning the discontinuance of social media use and the unfollowing behaviour on certain social media platforms. However, little is known about the unfollowing behaviour on Instagram and what causes users to unfollow public figures in particular. This study was the first trying to find out what factors influence users between the ages of 20 and 29 years of age to unfollow public figures. To this end, a total of nine qualitative guideline interviews were conducted with users recruited via Instagram. The interviews were analysed by means of an summary qualitative content analysis. Thereby, a total of eleven factors could be identified. The first factor relates to the negative feelings that arise when the content is received. The second and third factors relate to the public figure: behaviour and communication. The fourth, fifth and sixth factors relate to the frequency of posts, stories and the same content. The seventh, eighth, ninth, tenth and eleventh factors relate to content, but in different aspects: advertising, design, lack of identification, unfulfilled expectations and changes.The social media platform Instagram allows users to subscribe to various people from their immediate circle of acquaintances or to follow public figures. Recent research has identified reasons concerning the discontinuance of social media use and the unfollowing behaviour on certain social media platforms. However, little is known about the unfollowing behaviour on Instagram and what causes users to unfollow public figures in particular. This study was the first trying to find out what factors influence users between the ages of 20 and 29 years of age to unfollow public figures. To this end, a total of nine qualitative guideline interviews were conducted with users recruited via Instagram. The interviews were analysed by means of an summary qualitative content analysis. Thereby, a total of eleven factors could be identified. The first factor relates to the negative feelings that arise when the content is received. The second and third factors relate to the public figure: behaviour and communication. The fourth, fifth and sixth factors relate to the frequency of posts, stories and the same content. The seventh, eighth, ninth, tenth and eleventh factors relate to content, but in different aspects: advertising, design, lack of identification, unfulfilled expectations and changes

Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards ―desensitizing‖ prestige algorithms against cheating by spammers and other abusive use

Carthago Delenda Est: Co-opetitive Indirect Information Diffusion Model for Influence Operations on Online Social Media

For a state or non-state actor whose credibility is bankrupt, relying on bots to conduct non-attributable, non-accountable, and seemingly-grassroots-but-decentralized-in-actuality influence/information operations (info ops) on social media can help circumvent the issue of trust deficit while advancing its interests. Planning and/or defending against decentralized info ops can be aided by computational simulations in lieu of ethically-fraught live experiments on social media. In this study, we introduce Diluvsion, an agent-based model for contested information propagation efforts on Twitter-like social media. The model emphasizes a user's belief in an opinion (stance) being impacted by the perception of potentially illusory popular support from constant incoming floods of indirect information, floods that can be cooperatively engineered in an uncoordinated manner by bots as they compete to spread their stances. Our model, which has been validated against real-world data, is an advancement over previous models because we account for engagement metrics in influencing stance adoption, non-social tie spreading of information, neutrality as a stance that can be spread, and themes that are analogous to media's framing effect and are symbiotic with respect to stance propagation. The strengths of the Diluvsion model are demonstrated in simulations of orthodox info ops, e.g., maximizing adoption of one stance; creating echo chambers; inducing polarization; and unorthodox info ops, e.g., simultaneous support of multiple stances as a Trojan horse tactic for the dissemination of a theme.Comment: 60 pages, 9 figures, 1 tabl

On Measuring Social Dynamics of Online Social Media

Due to the complex nature of human behaviour and to our inability to directly measure thoughts and feelings, social psychology has long struggled for empirical grounding for its theories and models. Traditional techniques involving groups of people in controlled environments are limited to small numbers and may not be a good analogue for real social interactions in natural settings due to their controlled and artificial nature. Their application as a foundation for simulation of social processes suffers similarly. The proliferation of online social media offers new opportunities to observe social phenomena “in the wild” that have only just begun to be realised. To date, analysis of social media data has been largely focussed on specific, commercially relevant goals (such as sentiment analysis) that are of limited use to social psychology, and the dynamics critical to an understanding of social processes is rarely addressed or even present in collected data. This thesis addresses such shortfalls by: (i) presenting a novel data collection strategy and system for rich dynamic data from communities operating on Twitter; (ii) a data set encompassing longitudinal dynamic information over two and a half years from the online pro-ana (pro-anorexia) movement; and (iii) two approaches to identifying active social psychological processes in collections of online text and network metadata: an approach linking traditional psychometric studies with topic models and an algorithm combining community detection in user networks with topic models of the social media text they generate, enabling identification of community specific topic usage

Netmodern: Interventions in Digital Sociology

The techno-economic grid of the Internet looks set to fulfil its autopoietic potentials as a global and multi-dimensionally immersive knowledge and memory archival network. This research project moves through a series of Digital Sociology case studies that mimic the changes in paradigms of the WWW from 2005-2010 in the forms of Web 1.0 to 2.0 and beyond to augmented reality and the cloud. Netmodern social theory is an emergent and speculative product of the research findings of this thesis and the subjective experiences of the researcher in experiencing and explaining digital realities in the research. All of the case studies employ practice-based approaches of original investigation through digital interventions completely immersed in particular waves of innovation and change. The role of the researcher shifts from administrator to mediator and observer as the very fabric of the social web transforms and evolves. The suggestion of the research findings is that you need to actually look at everything differently in order to study the research objects of emergent social agency and forms in digital media. Existing forms of critical analysis and methodological frameworks, particularly those concerned with conceptual models of media literacy or collective intelligence are insufficient as explanatory methods. Studying media literacy is most concerned with ‘how’ we create and interact in online social life beyond issues of simple accessibility. The focus of collective intelligence research is ‘what’ knowledge is available for interaction and a canvas for relationships between agency and knowledge forms. All of the case studies in this research project speak to and critique the intersections and relationships of emergent social agency and forms prevalent in Digital Sociology. The collective case studies explore online academic communities (BlogScholar), agency and popularity in the Twitter social network (Twae) and a variety of representations of collective intelligence in action (Web 2.0 cases studies). The research results suggest that the Internet is not so much intersecting with as it is being culture, economy, and technology

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience