Twitter reciprocal reply networks exhibit assortativity with respect to happiness

The advent of social media has provided an extraordinary, if imperfect, 'big data' window into the form and evolution of social networks. Based on nearly 40 million message pairs posted to Twitter between September 2008 and February 2009, we construct and examine the revealed social network structure and dynamics over the time scales of days, weeks, and months. At the level of user behavior, we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression. We find users' average happiness scores to be positively and significantly correlated with those of users one, two, and three links away. We strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness. We also find evidence that more well connected users write happier status updates, with a transition occurring around Dunbar's number. More generally, our work provides evidence of a social sub-network structure within Twitter and raises several methodological points of interest with regard to social network reconstructions.Comment: 22 pages, 21 figures, 5 tables, In press at the Journal of Computational Scienc

An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks

Many real world, complex phenomena have underlying structures of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of topological features and node attributes can improve link prediction. We provide an approach to predicting future links by applying the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine a large dynamic social network with over $10^6$ nodes (Twitter reciprocal reply networks), both as a test of our general method and as a problem of scientific interest in itself. Our method exhibits fast convergence and high levels of precision for the top twenty predicted links. Based on our findings, we suggest possible factors which may be driving the evolution of Twitter reciprocal reply networks.Comment: 17 pages, 12 figures, 4 tables, Submitted to the Journal of Computational Scienc

Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science

Replication is an essential requirement for scientific discovery. The current study aims to generalize and replicate 10 propositions made in previous Twitter studies using a representative dataset. Our findings suggest 6 out of 10 propositions could not be replicated due to the variations of data collection, analytic strategies employed, and inconsistent measurements. The study’s contributions are twofold: First, it systematically summarized and assessed some important claims in the field, which can inform future studies. Second, it proposed a feasible approach to generating a random sample of Twitter users and its associated ego networks, which might serve as a solution for answering social-scientific questions at the individual level without accessing the complete data archive.published_or_final_versio

Dynamics of Trust Reciprocation in Heterogenous MMOG Networks

Understanding the dynamics of reciprocation is of great interest in sociology and computational social science. The recent growth of Massively Multi-player Online Games (MMOGs) has provided unprecedented access to large-scale data which enables us to study such complex human behavior in a more systematic manner. In this paper, we consider three different networks in the EverQuest2 game: chat, trade, and trust. The chat network has the highest level of reciprocation (33%) because there are essentially no barriers to it. The trade network has a lower rate of reciprocation (27%) because it has the obvious barrier of requiring more goods or money for exchange; morever, there is no clear benefit to returning a trade link except in terms of social connections. The trust network has the lowest reciprocation (14%) because this equates to sharing certain within-game assets such as weapons, and so there is a high barrier for such connections because they require faith in the players that are granted such high access. In general, we observe that reciprocation rate is inversely related to the barrier level in these networks. We also note that reciprocation has connections across the heterogeneous networks. Our experiments indicate that players make use of the medium-barrier reciprocations to strengthen a relationship. We hypothesize that lower-barrier interactions are an important component to predicting higher-barrier ones. We verify our hypothesis using predictive models for trust reciprocations using features from trade interactions. Using the number of trades (both before and after the initial trust link) boosts our ability to predict if the trust will be reciprocated up to 11% with respect to the AUC

개인 사회망 네트워크 분석 기반 온라인 사회 공격자 탐지

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 김종권.In the last decade we have witnessed the explosive growth of online social networking services (SNSs) such as Facebook, Twitter, Weibo and LinkedIn. While SNSs provide diverse benefits – for example, fostering inter-personal relationships, community formations and news propagation, they also attracted uninvited nuiance. Spammers abuse SNSs as vehicles to spread spams rapidly and widely. Spams, unsolicited or inappropriate messages, significantly impair the credibility and reliability of services. Therefore, detecting spammers has become an urgent and critical issue in SNSs. This paper deals with spamming in Twitter and Weibo. Instead of spreading annoying messages to the public, a spammer follows (subscribes to) normal users, and followed a normal user. Sometimes a spammer makes link farm to increase target accounts explicit influence. Based on the assumption that the online relationships of spammers are different from those of normal users, I proposed classification schemes that detect online social attackers including spammers. I firstly focused on ego-network social relations and devised two features, structural features based on Triad Significance Profile (TSP) and relational semantic features based on hierarchical homophily in an ego-network. Experiments on real Twitter and Weibo datasets demonstrated that the proposed approach is very practical. The proposed features are scalable because instead of analyzing the whole network, they inspect user-centered ego-networks. My performance study showed that proposed methods yield significantly better performance than prior scheme in terms of true positives and false positives.최근 우리는 Facebook, Twitter, Weibo, LinkedIn 등의 다양한 사회 관계망 서비스가 폭발적으로 성장하는 현상을 목격하였다. 하지만 사회 관계망 서비스가 개인과 개인간의 관계 및 커뮤니티 형성과 뉴스 전파 등의 여러 이점을 제공해 주고 있는데 반해 반갑지 않은 현상 역시 발생하고 있다. 스패머들은 사회 관계망 서비스를 동력 삼아 스팸을 매우 빠르고 넓게 전파하는 식으로 악용하고 있다. 스팸은 수신자가 원치 않는 메시지들을 일컽는데 이는 서비스의 신뢰도와 안정성을 크게 손상시킨다. 따라서, 스패머를 탐지하는 것이 현재 소셜 미디어에서 매우 긴급하고 중요한 문제가 되었다. 이 논문은 대표적인 사회 관계망 서비스들 중 Twitter와 Weibo에서 발생하는 스패밍을 다루고 있다. 이러한 유형의 스패밍들은 불특정 다수에게 메시지를 전파하는 대신에, 많은 일반 사용자들을 '팔로우(구독)'하고 이들로부터 '맞 팔로잉(맞 구독)'을 이끌어 내는 것을 목적으로 하기도 한다. 때로는 link farm을 이용해 특정 계정의 팔로워 수를 높이고 명시적 영향력을 증가시키기도 한다. 스패머의 온라인 관계망이 일반 사용자의 온라인 사회망과 다를 것이라는 가정 하에, 나는 스패머들을 포함한 일반적인 온라인 사회망 공격자들을 탐지하는 분류 방법을 제시한다. 나는 먼저 개인 사회망 내 사회 관계에 주목하고 두 가지 종류의 분류 특성을 제안하였다. 이들은 개인 사회망의 Triad Significance Profile (TSP)에 기반한 구조적 특성과 Hierarchical homophily에 기반한 관계 의미적 특성이다. 실제 Twitter와 Weibo 데이터셋에 대한 실험 결과는 제안한 방법이 매우 실용적이라는 것을 보여준다. 제안한 특성들은 전체 네트워크를 분석하지 않아도 개인 사회망만 분석하면 되기 때문에 scalable하게 측정될 수 있다. 나의 성능 분석 결과는 제안한 기법이 기존 방법에 비해 true positive와 false positive 측면에서 우수하다는 것을 보여준다.1 Introduction 1 2 Related Work 6 2.1 OSN Spammer Detection Approaches 6 2.1.1 Contents-based Approach 6 2.1.2 Social Network-based Approach 7 2.1.3 Subnetwork-based Approach 8 2.1.4 Behavior-based Approach 9 2.2 Link Spam Detection 10 2.3 Data mining schemes for Spammer Detection 10 2.4 Sybil Detection 12 3 Triad Significance Profile Analysis 14 3.1 Motivation 14 3.2 Twitter Dataset 18 3.3 Indegree and Outdegree of Dataset 20 3.4 Twitter spammer Detection with TSP 22 3.5 TSP-Filtering 27 3.6 Performance Evaluation of TSP-Filtering 29 4 Hierarchical Homophily Analysis 33 4.1 Motivation 33 4.2 Hierarchical Homophily in OSN 37 4.2.1 Basic Analysis of Datasets 39 4.2.2 Status gap distribution and Assortativity 44 4.2.3 Hierarchical gap distribution 49 4.3 Performance Evaluation of HH-Filtering 53 5 Overall Performance Evaluation 58 6 Conclusion 63 Bibliography 65Docto

Information diffusion in online social networks: a simulation experiment

The advent of online social networks has completely transformed the way we communicate, with news, opinions, and ideas now spreading faster than ever before (Guille et al., 2013; Lee et al., 2022). That online social networks have a profound impact on the spread of information suggests further investigation of the relationship between network structure and information diffusion (Light & Moody, 2020). This honors thesis investigates degree assortativity – a measure of large-scale network structure that has often only been a footnote in relevant literature on infor- mation diffusion in online social networks – and its effect on the speed of informa- tion diffusion in online social networks. Two rewiring algorithms (Xulvi-Brunet & Sokolov, 2005) were applied to rewire a Facebook friend circle (n = 44) with varying degree assortativity, ranging from approximately −0.7 to 0.4. For each of the 160 rewired graphs, a random node was selected to infect (i.e., spread information to) its neighbors with probabilities ranging from 10 to 50 percent, and the number of infected nodes after each round of diffusion was recorded. Results suggest that degree assortativity and the speed of information dif- fusion have a strong inverse relationship – disassortative networks spread the same information faster. Moreover, degree assortativity appears to drive the speed of in- formation diffusion more than its correlates, clustering coefficient and average path length (Xulvi-Brunet & Sokolov, 2005)

Emoticon-based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo

Recent decades have witnessed online social media being a big-data window for quantificationally testifying conventional social theories and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden "ambivalent users" are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights or at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and thus serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift of words adopted in ambivalent tweets is more evident than usual and exhibits a clear "negative to positive" pattern. The above observations, though being promiscuous seemingly, actually point to the self regulation of negative mood in Weibo, which could find its base from the emotion management theories in sociology but makes an interesting extension to the online environment. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing.Comment: Data sets can be downloaded freely from www.datatang.com/data/47207 or http://pan.baidu.com/s/1mg67cbm. Any issues feel free to contact [email protected]