Search Results (9)

The evolution of social bots, i.e., accounts on social media platforms controlled by malicious software, is making them increasingly more challenging to discover. A practical solution is to explore the adversarial nature of novel bots and find the vulnerability of bot detectors in simulations in advance. However, current studies fail to realistically simulate the environment and bots’ actions, thus not effectively representing the competition between novel bots and bot detectors. Hence, we propose a new method for modeling the impact of bot actions and develop a new bot strategy to simulate various evolved bots within a large social network. Specifically, a bot influence model and a user engagement model are introduced to simulate the growth of followers, retweets, and mentions. Additionally, a profile editor and a target preselection mechanism are proposed to more accurately simulate the behavior of evolved bots. The effectiveness of the bots and two representative bot detectors are verified using adversarial simulations and the real-world dataset. In simulated adversarial scenarios against both RF-based and GNN-based detection models, the proposed approach achieves survival rates of 99.7% and 85.9%, respectively. The simulation results indicate that, despite utilizing the bots’ profile data, user-generated content, and graph information, the detectors failed to identify all variations of the bots and mitigate their impact. More importantly, for the first time, it is found that certain types of bots outperform those usually deemed more advanced in ablation experiments, demonstrating that such “penetration testing” can indeed reveal vulnerabilities in the detectors. Full article

During the COVID-19 pandemic, pro-vaccine and anti-vaccine groups emerged, influencing others to vaccinate or abstain and leading to polarized debates. Due to incomplete user data and the complexity of social network interactions, understanding the dynamics of these discussions is challenging. This study aims to discover and quantify the factors driving the controversy related to vaccine stances across Kuwaiti social networks. To tackle these challenges, a graph convolutional network (GCN) and feature propagation (FP) were utilized to accurately detect users’ stances despite incomplete features, achieving an accuracy of 96%. Additionally, the random walk controversy (RWC) score was employed to quantify polarization points within the social networks. Experiments were conducted using a dataset of vaccine-related retweets and discussions from X (formerly Twitter) during the Kuwait COVID-19 vaccine rollout period. The analysis revealed high polarization periods correlating with specific vaccination rates and governmental announcements. This research provides a novel approach to accurately detecting user stances in low-resource languages like the Kuwaiti dialect without the need for costly annotations, offering valuable insights to help policymakers understand public opinion and address misinformation effectively. Full article

The viral spread of online content can lead to unexpected consequences such as extreme opinions about a brand or consumers’ enthusiasm for a product. This makes the prediction of viral content’s future popularity an important problem, especially for digital marketers, as well as for managers of social platforms. It is not surprising that conventional methods, which heavily rely on either hand-crafted features or unrealistic assumptions, are insufficient in dealing with this challenging problem. Even state-of-art graph-based approaches are either inefficient to work with large-scale cascades or unable to explain what spread mechanisms are learned by the model. This paper presents a temporal-spatial cascade convolutional learning framework called ViralGCN, not only to address the challenges of existing approaches but also to try to provide some insights into actual mechanisms of viral spread from the perspective of artificial intelligence. We conduct experiments on the real-world dataset (i.e., to predict the retweet popularity of micro-blogs on Weibo). Compared to the existing approaches, ViralGCN possesses the following advantages: the flexible size of the input cascade graph, a coherent method for processing both structural and temporal information, and an intuitive and interpretable deep learning architecture. Moreover, the exploration of the learned features also provides valuable clues for managers to understand the elusive mechanisms of viral spread as well as to devise appropriate strategies at early stages. By using the visualization method, our approach finds that both broadcast and structural virality contribute to online content going viral; the cascade with a gradual descent or ascent-then-descent evolving pattern at the early stage is more likely to gain significant eventual popularity, and even the timing of users participating in the cascade has an effect on future popularity growth. Full article

Social media platforms have revolutionized information exchange and socialization in today’s world. Twitter, as one of the prominent platforms, enables users to connect with others and express their opinions. This study focuses on analyzing user engagement levels on Twitter using graph mining and clustering techniques. We measure user engagement based on various tweet attributes, including retweets, replies, and more. Specifically, we explore the strength of user connections in Twitter networks by examining the diversity of edges. Our approach incorporates graph mining models that assign different weights to evaluate the significance of each connection. Additionally, clustering techniques are employed to group users based on their engagement patterns and behaviors. Statistical analysis was conducted to assess the similarity between user profiles, as well as attributes, such as friendship, followings, and interactions within the Twitter social network. The findings highlight the discovery of closely linked user groups and the identification of distinct clusters based on engagement levels. This research emphasizes the importance of understanding both individual and group behaviors in comprehending user engagement dynamics on Twitter. Full article

This paper contributes to the Special Issue on Communication for the Digital Media Age by investigating the factors that influence the management of political information on online news media platforms, specifically Twitter and Weibo. Using the recent “G20 Summit” as a case study, this study employs a mixed-methods approach that incorporates both deductive and inductive reasoning. Social network analysis (SNA) and graph theory are used to evaluate specific social relationships in the context of the G20 summit, while a combination of structured and content (semantic) analysis is performed. The findings indicate that individual power is becoming increasingly important in the age of online news media. Individuals contribute significantly to the diffusion of information and may play a decisive role in the future. The study also finds that the frequency of retweets increases as the reciprocity ratio increases, and mentions may be the most effective method for delivering political news on online news media platforms. Practical implications suggest strategies for managing information diffusion effectively. Additionally, this study provides insights into effective information diffusion on online news media platforms that can be utilized in health communication management during the COVID-19 era. This study expands theoretical understanding by investigating the role of individual power in the age of online news media and enriching the literature on online news media through the use of structured and content analysis based on social network analysis. Full article

Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. Methods: This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach identified an echo chamber (red community, 66K accounts) focused on a few topics with a triad of anti-Covid, right-wing populism and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community (113K accounts) was much more heterogeneous and showed “normal” communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship. Full article

The goal of this work is to evaluate a deep learning algorithm that has been designed to predict the topological evolution of dynamic complex non-Euclidean graphs in discrete–time in which links are labeled with communicative messages. This type of graph can represent, for example, social networks or complex organisations such as the networks associated with Industry 4.0. In this paper, we first introduce the formal geometric deep lean learning algorithm in its essential form. We then propose a methodology to systematically mine the data generated in social media Twitter, which resembles these complex topologies. Finally, we present the evaluation of a geometric deep lean learning algorithm that allows for link prediction within such databases. The evaluation results show that this algorithm can provide high accuracy in the link prediction of a retweet social network. Full article

Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph that is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactions. Initial findings showed the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet. Full article

Social media has driven a sea change in the way users view and participate in sporting events through the media. In the digital medium, identifying the profiles with the greatest influential capacity is a key issue. Using the analytical hierarchy process (AHP), the aim of our research was to identify the most influential Twitter accounts in a major sporting event: The Track Cycling World Cups. The competitions from the years 2016 and 2018 were analysed, downloading all the tweets that included the official hashtag of each event and drawing up the graph of mentions and retweets. After reviewing the literature, activity, authority and popularity were defined as dimensions to assess influence, and two subcriteria were chosen as measures for each of them. Activity was measured by number of tweets and outdegree, authority by retweets and PageRank, and popularity by number of followers and indegree. By consulting experts following the AHP approach, various weights were assigned to these measures, resulting in authority as the most influential. With this weighting, the accounts with the greatest influence on Twitter turned out to be those related to organisation of the event and those of the athletes taking part. Full article