On the Aggression Diffusion Modeling and Minimization in Twitter

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

Framework design for competitive marketing mechanism in Iraqi social media

Several organizations in Iraq manufacture similar commodities in this aggressive social trading. The objective of these organizations is diffusing information about their commodities publicly for popularity of the commodities in social media. More returns result in popular commodities and vice versa. The development of a framework incorporating two organizations engaging to broaden the information to the large media has been undertaken. The organizations first identified their initial seed points concurrently and then data was scattered as per the Independent Cascade Model (ICM). The major objective of the organizations is the identification of seed points for the diffusion of data to several points in social media. Significant is also how fast data diffusion can be done. Data effect will arise from either none, one or more nodes in a social interconnection. Evaluation is also accomplished on the number of fraction parts in various sections are affected by the different rates of data diffusion. The simulation result for suggested framework presented better outcomes result for random network 1 and random network 2 comparing with regular network. This framework is used a Hotellingframwork of competition.

Spread of tweets in climate discussions: A case study of the 2019 Nobel Peace Prize announcement

Characterising the spreading of ideas within echo chambers is essential for understanding polarisation. In this article, we explore the characteristics of popular and viral content in climate change discussions on Twitter around the 2019 announcement of the Nobel Peace Prize, where we find the retweet network of users to be polarised into two well-separated groups of activists and sceptics. Operationalising popularity as the number of retweets and virality as the spreading probability inferred using an independent cascade model, we find that the viral themes echo and differ from the popular themes in interesting ways. Most importantly, we find that the most viral themes in the two groups reflect different types of bonds that tie the community together, yet both function to enhance ingroup connections while repulsing outgroup engagement. With this, our study sheds light, from an information-spreading perspective, on the formation and upkeep of echo chambers in climate discussions.

Schemes of propagation models and source estimators for rumor source detection in online social networks: A short survey of a decade of research

Recent years have seen various rumor diffusion models being assumed in detection of rumor source research of the online social network. Diffusion model is arguably considered as a very important and challenging factor for source detection in networks, but it is less studied. This paper provides an overview of three representative schemes of modeling the pattern of rumor propagation as well as three major schemes of rumor source estimator in the Independent Cascade-based model, the Epidemic-based model, and the Learning-based model, respectively, since their inception a decade ago.

Information Contagion and Stock Price Crash Risk

We introduce continuity and temporariness into the independent cascade model to depict information diffusion in a social network. Investor behavior changes are determined according to the process of information diffusion, and the investment portfolio model consisting of sentiments is proposed to reveal the fire sales of stocks and the resulting stock price crash risk. Therefore, the relationship between information diffusion and stock price crash risk is established, and the contagion of stock price crash risk is analyzed from the perspective of information diffusion. Furthermore, some immunization strategies of networks are compared to prevent stock price crash risk. The results show that the tendency of stock price crash risk is consistent with that of information diffusion, which indicates that information diffusion before the fire sales is the key to triggering stock price crash risk. Moreover, investors with many ties contribute more to information diffusion than others; hence, immunization strategies of networks based on global information are more effective in preventing stock price crash risk than that based on local information. This study provides a new perspective for the study of contagion risk in the stock market, and it hints at the possibility of regulatory intervention to prevent stock price crash risk.

Positive Influence Maximization in the Signed Social Networks Considering Polarity Relationship and Propagation Probability

The purpose of influence maximization problem is to select a small seed set to maximize the number of nodes influenced by the seed set. For viral marketing, the problem of influence maximization plays a vital role. Current works mainly focus on the unsigned social networks, which include only positive relationship between users. However, the influence maximization in the signed social networks including positive and negative relationships between users is still a challenging issue. Moreover, the existing works pay more attention to the positive influence. Therefore, this paper first analyzes the positive maximization influence in the signed social networks. The purpose of this problem is to select the seed set with the most positive influence in the signed social networks. Afterwards, this paper proposes a model that incorporates the state of node, the preference of individual and polarity relationship, called Independent Cascade with the Negative and Polarity (ICWNP) propagation model. On the basis of the ICWNP model, this paper proposes a Greedy with ICWNP algorithm. Finally, on four real social networks, experimental results manifest that the proposed algorithm has higher accuracy and efficiency than the related methods.