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27 pages, 7617 KiB  
Article
Emoji-Driven Sentiment Analysis for Social Bot Detection with Relational Graph Convolutional Networks
by Kaqian Zeng, Zhao Li and Xiujuan Wang
Sensors 2025, 25(13), 4179; https://doi.org/10.3390/s25134179 - 4 Jul 2025
Viewed by 457
Abstract
The proliferation of malicious social bots poses severe threats to cybersecurity and social media information ecosystems. Existing detection methods often overlook the semantic value and emotional cues conveyed by emojis in user-generated tweets. To address this gap, we propose ESA-BotRGCN, an emoji-driven multi-modal [...] Read more.
The proliferation of malicious social bots poses severe threats to cybersecurity and social media information ecosystems. Existing detection methods often overlook the semantic value and emotional cues conveyed by emojis in user-generated tweets. To address this gap, we propose ESA-BotRGCN, an emoji-driven multi-modal detection framework that integrates semantic enhancement, sentiment analysis, and multi-dimensional feature modeling. Specifically, we first establish emoji–text mapping relationships using the Emoji Library, leverage GPT-4 to improve textual coherence, and generate tweet embeddings via RoBERTa. Subsequently, seven sentiment-based features are extracted to quantify statistical disparities in emotional expression patterns between bot and human accounts. An attention gating mechanism is further designed to dynamically fuse these sentiment features with user description, tweet content, numerical attributes, and categorical features. Finally, a Relational Graph Convolutional Network (RGCN) is employed to model heterogeneous social topology for robust bot detection. Experimental results on the TwiBot-20 benchmark dataset demonstrate that our method achieves a superior accuracy of 87.46%, significantly outperforming baseline models and validating the effectiveness of emoji-driven semantic and sentiment enhancement strategies. Full article
(This article belongs to the Section Sensor Networks)
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26 pages, 2931 KiB  
Article
CB-MTE: Social Bot Detection via Multi-Source Heterogeneous Feature Fusion
by Meng Cheng, Yuzhi Xiao, Tao Huang, Chao Lei and Chuang Zhang
Sensors 2025, 25(11), 3549; https://doi.org/10.3390/s25113549 - 4 Jun 2025
Viewed by 544
Abstract
Social bots increasingly mimic real users and collaborate in large-scale influence campaigns, distorting public perception and making their detection both critical and challenging. Traditional bot detection methods, constrained by single-source features, often fail to capture the complete behavioral and contextual characteristics of social [...] Read more.
Social bots increasingly mimic real users and collaborate in large-scale influence campaigns, distorting public perception and making their detection both critical and challenging. Traditional bot detection methods, constrained by single-source features, often fail to capture the complete behavioral and contextual characteristics of social bots, especially their dynamic behavioral evolution and group coordination tactics, resulting in feature incompleteness and reduced detection performance. To address this challenge, we propose CB-MTE, a social bot detection framework based on multi-source heterogeneous feature fusion. CB-MTE adopts a hierarchical architecture: user metadata is used to construct behavioral portraits, deep semantic representations are extracted from textual content via DistilBERT, and community-aware graph embeddings are learned through a combination of random walk and Skip-gram modeling. To mitigate feature redundancy and preserve structural consistency, manifold learning is applied for nonlinear dimensionality reduction, ensuring both local and global topology are maintained. Finally, a CatBoost-based collaborative reasoning mechanism enhances model robustness through ordered target encoding and symmetric tree structures. Experiments on the TwiBot-22 benchmark dataset demonstrate that CB-MTE significantly outperforms mainstream detection models in recognizing dynamic behavioral traits and detecting collaborative bot activities. These results confirm the framework’s capability to capture the complete behavioral and contextual characteristics of social bots through multi-source feature integration. Full article
(This article belongs to the Section Sensors and Robotics)
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21 pages, 978 KiB  
Article
Prevention Is Better than Cure: Exposing the Vulnerabilities of Social Bot Detectors with Realistic Simulations
by Rui Jin and Yong Liao
Appl. Sci. 2025, 15(11), 6230; https://doi.org/10.3390/app15116230 - 1 Jun 2025
Viewed by 561
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Artificial Neural Network and Deep Learning in Cybersecurity)
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17 pages, 676 KiB  
Article
Quantifying Bot Impact: An Information-Theoretic Analysis of Complexity and Uncertainty in Online Political Communication Dynamics
by Beril Bulat and Martin Hilbert
Entropy 2025, 27(6), 573; https://doi.org/10.3390/e27060573 - 28 May 2025
Viewed by 491
Abstract
Bots have become increasingly prevalent in the digital sphere and have taken up a proactive role in shaping democratic processes. While previous studies have focused on their influence at the individual level, their potential macro-level impact on communication dynamics remains underexplored. This study [...] Read more.
Bots have become increasingly prevalent in the digital sphere and have taken up a proactive role in shaping democratic processes. While previous studies have focused on their influence at the individual level, their potential macro-level impact on communication dynamics remains underexplored. This study adopts an information-theoretic approach from dynamical systems theory to examine the role of political bots shaping the dynamics of an online political discussion on Twitter. We quantify the components of this dynamic process in terms of its complexity, predictability, and its entropy rate, or the remaining uncertainty. Findings suggest that bot activity is associated with increased complexity and, simultaneously, with more uncertainty in the structural dynamics of online political communication. While our dataset features earlier-generation bots, findings foreshadow the possibility for even more complex and uncertain online politics in the age of sophisticated and autonomous generative AI agents. Our presented framework showcases how this can be studied with the use of information-theoretic measures from dynamical systems theory. Full article
(This article belongs to the Special Issue Statistical Physics Approaches for Modeling Human Social Systems)
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24 pages, 4056 KiB  
Article
Unveiling the Ultimate Meme Recipe: Image Embeddings for Identifying Top Meme Templates from r/Memes
by Jan Sawicki
J. Imaging 2025, 11(5), 132; https://doi.org/10.3390/jimaging11050132 - 23 Apr 2025
Viewed by 1270
Abstract
Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in [...] Read more.
Meme analysis, particularly identifying top meme templates, is crucial for understanding digital culture, communication trends, and the spread of online humor, as memes serve as units of cultural transmission that shape public discourse. Tracking popular templates enables researchers to examine their role in social engagement, ideological framing, and viral dynamics within digital ecosystems. This study explored the viral nature of memes by analyzing a large dataset of over 1.5 million meme submissions from Reddit’s r/memes subreddit, spanning from January 2021 to July 2024. The focus was on uncovering the most popular meme templates by applying advanced image processing techniques. Apart from building an overall understanding of the memesphere, the main contribution was a selection of top meme templates providing a recipe for the best meme template for the meme creators (memesters). Using Vision Transformer (ViT) models, visual features of memes were analyzed without the influence of text, and memes were grouped into 1000 clusters that represented distinct templates. By combining image captioning and keyword extraction methods, key characteristics of the templates were identified, highlighting those with the most visual consistency. A deeper examination of the most popular memes revealed that factors like timing, cultural relevance, and references to current events played a significant role in their virality. Although user identity had limited influence on meme success, a closer look at contributors revealed an interesting pattern of a bot account and two prominent users. Ultimately, the study pinpointed the ten most popular meme templates, many of which were based on pop culture, offering insights into what makes a meme likely to go viral in today’s digital culture. Full article
(This article belongs to the Section Image and Video Processing)
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14 pages, 1532 KiB  
Article
The Impact of a Music- and Movement-Based Intervention on Motor Competence, Social Engagement, and Behavior in Children with Autism Spectrum Disorder
by Chayma Kanzari, Aymen Hawani, Karim Ben Ayed, Maher Mrayeh, Santo Marsigliante and Antonella Muscella
Children 2025, 12(1), 87; https://doi.org/10.3390/children12010087 - 14 Jan 2025
Viewed by 4183
Abstract
Background/Objectives: The main objective of this manuscript is to evaluate the effects of training, music, and movement intervention on motor functions, social engagement, and behaviors in autistic children. Methods: Twenty-one children with a diagnosis of mild autism spectrum disorder (ASD), with an age [...] Read more.
Background/Objectives: The main objective of this manuscript is to evaluate the effects of training, music, and movement intervention on motor functions, social engagement, and behaviors in autistic children. Methods: Twenty-one children with a diagnosis of mild autism spectrum disorder (ASD), with an age range of 5-to-13 years, were divided into two groups: the experimental group (n = 10) and the control group (n = 11). All participants were examined before (T0) and after the intervention (T1) to evaluate their motor functions (Bruininks–Oseretsky Motor Performance Test (BOT-2)), maladaptive behavior (RCS (Response to Challenge Scale)), and enjoyment and engagement (PACES (Physical Activity Enjoyment Scale)). Results: Statistical analysis showed that music and movement intervention significantly improved motor functions such as balance and bilateral coordination (p < 0.0001), social engagement (p = 0.002), and adaptive behaviors (p = 0.005) in children with ASD. Our research supports the feasibility of music and movement intervention and documents the interest in participating in children with ASD. Conclusions: This study demonstrates the benefits of movement and music interventions and can be considered a useful way to manage autism spectrum disorders in the future. Full article
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12 pages, 999 KiB  
Perspective
Collaborative Robots with Cognitive Capabilities for Industry 4.0 and Beyond
by Giulio Sandini, Alessandra Sciutti and Pietro Morasso
AI 2024, 5(4), 1858-1869; https://doi.org/10.3390/ai5040092 - 9 Oct 2024
Cited by 2 | Viewed by 2076
Abstract
The robots that entered the manufacturing sector in the second and third Industrial Revolutions (IR2 and IR3) were designed for carrying out predefined routines without physical interaction with humans. In contrast, IR4* robots (i.e., robots since IR4 and beyond) are supposed to interact [...] Read more.
The robots that entered the manufacturing sector in the second and third Industrial Revolutions (IR2 and IR3) were designed for carrying out predefined routines without physical interaction with humans. In contrast, IR4* robots (i.e., robots since IR4 and beyond) are supposed to interact with humans in a cooperative way for enhancing flexibility, autonomy, and adaptability, thus dramatically improving productivity. However, human–robot cooperation implies cognitive capabilities that the cooperative robots (CoBots) in the market do not have. The common wisdom is that such a cognitive lack can be filled in a straightforward way by integrating well-established ICT technologies with new AI technologies. This short paper expresses the view that this approach is not promising and suggests a different one based on artificial cognition rather than artificial intelligence, founded on concepts of embodied cognition, developmental robotics, and social robotics. We suggest giving these IR4* robots designed according to such principles the name CoCoBots. The paper also addresses the ethical problems that can be raised in cases of critical emergencies. In normal operating conditions, CoCoBots and human partners, starting from individual evaluations, will routinely develop joint decisions on the course of action to be taken through mutual understanding and explanation. In case a joint decision cannot be reached and/or in the limited case that an emergency is detected and declared by top security levels, we suggest that the ultimate decision-making power, with the associated responsibility, should rest on the human side, at the different levels of the organized structure. Full article
(This article belongs to the Special Issue Intelligent Systems for Industry 4.0)
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28 pages, 944 KiB  
Review
Orthorexia as an Eating Disorder Spectrum—A Review of the Literature
by Izabela Łucka, Artur Mazur, Anna Łucka, Izabela Sarzyńska, Julia Trojniak and Marta Kopańska
Nutrients 2024, 16(19), 3304; https://doi.org/10.3390/nu16193304 - 29 Sep 2024
Cited by 3 | Viewed by 4837
Abstract
Background: The purpose of this study is to compare and analyze research studies focused on orthorexia nervosa (ON) as a spectrum of eating disorders, and to summarize potential risk factors in different age and social groups. ON is characterized by an obsession with [...] Read more.
Background: The purpose of this study is to compare and analyze research studies focused on orthorexia nervosa (ON) as a spectrum of eating disorders, and to summarize potential risk factors in different age and social groups. ON is characterized by an obsession with healthy eating, which leads to a restrictive diet and health problems. Methods: Due to a lack of comprehensive analyses, this review re-examined studies from 2006 to 2023, initially retrieving 53,134 articles. Upon refining the criteria and risk factors for eating disorders, 34 notable records were identified. These studies employed diagnostic tools such as ORTO and BOT, focusing on risk factors for ON. Results: Results indicate that individuals suffering from eating disorders, losing weight, exercising heavily, developing relationship problems, and suffering from body dysmorphic disorder are at high risk of developing ON. A significant correlation was found between ON, BMI, and gender, but not between ON and OCD. Interestingly, ON symptoms appear to overlap with those of other eating disorders, such as anorexia and bulimia, especially in terms of obsessive control over dieting and fear of gaining weight, indicating a close relationship between the two. Conclusions: Interestingly, orthorexia nervosa may serve as a coping mechanism for anorexia, providing a sense of control. However, further research on its long-term effects is required. Full article
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28 pages, 6895 KiB  
Article
Alquist 5.0: Dialogue Trees Meet Generative Models, a Novel Approach for Enhancing SocialBot Conversations
by Ondrej Kobza, David Herel, Jan Cuhel, Tommaso Gargiani, Petr Marek and Jan Sedivy
Future Internet 2024, 16(9), 344; https://doi.org/10.3390/fi16090344 - 21 Sep 2024
Cited by 1 | Viewed by 1250
Abstract
This article introduces Alquist 5.0, our SocialBot that was designed for the Alexa Prize SocialBot Grand Challenge 5. Building upon previous iterations, we present the integration of our novel neural response generator (NRG) Barista within a hybrid architecture that combines traditional predefined dialogues [...] Read more.
This article introduces Alquist 5.0, our SocialBot that was designed for the Alexa Prize SocialBot Grand Challenge 5. Building upon previous iterations, we present the integration of our novel neural response generator (NRG) Barista within a hybrid architecture that combines traditional predefined dialogues with advanced neural response generation. We provide a comprehensive analysis of the current state-of-the-art NRGs and large language models (LLMs), leveraging these insights to enhance Barista’s capabilities. A key focus of our development was in ensuring the safety of our chatbot and implementing robust measures to prevent profanity and inappropriate content. Additionally, we incorporated a new search engine to improve information retrieval and response accuracy. Expanding the capabilities of our system, we designed Alquist 5.0 to accommodate multimodal devices, utilizing APL templates enriched with custom features to deliver an outstanding conversational experience complemented by an excellent user interface. This paper offers detailed insights into the development of Alquist 5.0, which effectively addresses evolving user demands while preserving its empathetic and knowledgeable conversational prowess across a wide range of topics. Full article
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35 pages, 15883 KiB  
Article
Bias and Cyberbullying Detection and Data Generation Using Transformer Artificial Intelligence Models and Top Large Language Models
by Yulia Kumar, Kuan Huang, Angelo Perez, Guohao Yang, J. Jenny Li, Patricia Morreale, Dov Kruger and Raymond Jiang
Electronics 2024, 13(17), 3431; https://doi.org/10.3390/electronics13173431 - 29 Aug 2024
Cited by 8 | Viewed by 5715
Abstract
Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly on social media platforms like X (formerly Twitter), to address the prevalent cyberbullying on these platforms. This research investigates the effectiveness of [...] Read more.
Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly on social media platforms like X (formerly Twitter), to address the prevalent cyberbullying on these platforms. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Utilizing state-of-the-art LLMs and AI tools such as ChatGPT-4, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and experimentally quantized. This study focuses on intersectional cyberbullying and multilabel classification to detect both bias and cyberbullying. Additionally, it proposes two prototype applications: one that detects cyberbullying using an intersectional approach and the innovative CyberBulliedBiasedBot that combines the generation and detection of biased and cyberbullying content. Full article
(This article belongs to the Special Issue Emerging Artificial Intelligence Technologies and Applications)
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16 pages, 6063 KiB  
Article
The Usage of Twitter (Now X) Amplifiers in the European Elections of 2019
by Thomai Voulgari, Alexandros K. Angelidis, Charalampos Bratsas, Rigas Kotsakis, Andreas Veglis and Antonis Skamnakis
Journal. Media 2024, 5(3), 951-966; https://doi.org/10.3390/journalmedia5030060 - 12 Jul 2024
Viewed by 2043
Abstract
The aim of this study is to investigate how amplifiers are used in Twitter (now called “X”) during election campaigns. Specifically, the main purpose is to identify the role and engagement of Twitter amplifiers in the 2019 European elections, the visibility [...] Read more.
The aim of this study is to investigate how amplifiers are used in Twitter (now called “X”) during election campaigns. Specifically, the main purpose is to identify the role and engagement of Twitter amplifiers in the 2019 European elections, the visibility of political parties and leaders, and the way in which automated tools are used to manipulate public opinion by influencing voting decisions. The countries considered in the study are two economic powers of Western Europe, France and Germany, as well as two countries of the European South, which are affected by the economic and financial crisis, Greece and Italy. The countries from Southern Europe were included in the sample as they are often used by mass media as political campaign tools. This paper emphasizes the Twitter platform through which the data collection was implemented using the official API of the social networking tool, focusing on the 2019 European elections. We collected data on 88 party leaders and MEP candidates between 10 May and 30 May 2019, as well as on 44,651 accounts that retweeted them. We concluded using 237,813 election-related tweets and used network theory to analyze and visualize the data. The results demonstrate that all political parties use amplifiers to promote their tweets, and some use the same amplifiers between different countries. Full article
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17 pages, 861 KiB  
Article
FedKG: A Knowledge Distillation-Based Federated Graph Method for Social Bot Detection
by Xiujuan Wang, Kangmiao Chen, Keke Wang, Zhengxiang Wang, Kangfeng Zheng and Jiayue Zhang
Sensors 2024, 24(11), 3481; https://doi.org/10.3390/s24113481 - 28 May 2024
Cited by 2 | Viewed by 1672
Abstract
Malicious social bots pose a serious threat to social network security by spreading false information and guiding bad opinions in social networks. The singularity and scarcity of single organization data and the high cost of labeling social bots have given rise to the [...] Read more.
Malicious social bots pose a serious threat to social network security by spreading false information and guiding bad opinions in social networks. The singularity and scarcity of single organization data and the high cost of labeling social bots have given rise to the construction of federated models that combine federated learning with social bot detection. In this paper, we first combine the federated learning framework with the Relational Graph Convolutional Neural Network (RGCN) model to achieve federated social bot detection. A class-level cross entropy loss function is applied in the local model training to mitigate the effects of the class imbalance problem in local data. To address the data heterogeneity issue from multiple participants, we optimize the classical federated learning algorithm by applying knowledge distillation methods. Specifically, we adjust the client-side and server-side models separately: training a global generator to generate pseudo-samples based on the local data distribution knowledge to correct the optimization direction of client-side classification models, and integrating client-side classification models’ knowledge on the server side to guide the training of the global classification model. We conduct extensive experiments on widely used datasets, and the results demonstrate the effectiveness of our approach in social bot detection in heterogeneous data scenarios. Compared to baseline methods, our approach achieves a nearly 3–10% improvement in detection accuracy when the data heterogeneity is larger. Additionally, our method achieves the specified accuracy with minimal communication rounds. Full article
(This article belongs to the Section Sensors and Robotics)
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12 pages, 1867 KiB  
Article
Experimental Evaluation: Can Humans Recognise Social Media Bots?
by Maxim Kolomeets, Olga Tushkanova, Vasily Desnitsky, Lidia Vitkova and Andrey Chechulin
Big Data Cogn. Comput. 2024, 8(3), 24; https://doi.org/10.3390/bdcc8030024 - 26 Feb 2024
Cited by 5 | Viewed by 4516
Abstract
This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult for human annotators to [...] Read more.
This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult for human annotators to detect them better than random selection. As a result, obtaining a ground-truth dataset with human annotation is not possible, which leads to supervised machine-learning models inheriting annotation errors. To test this hypothesis, we conducted an experiment where humans were tasked with recognizing malicious bots on the VKontakte social network. We then compared the “human” answers with the “ground-truth” bot labels (‘a bot’/‘not a bot’). Based on the experiment, we evaluated the bot detection efficiency of annotators in three scenarios typical for cybersecurity but differing in their detection difficulty as follows: (1) detection among random accounts, (2) detection among accounts of a social network ‘community’, and (3) detection among verified accounts. The study showed that humans could only detect simple bots in all three scenarios but could not detect more sophisticated ones (p-value = 0.05). The study also evaluates the limits of hypothetical and existing bot detection systems that leverage non-expert-labelled datasets as follows: the balanced accuracy of such systems can drop to 0.5 and lower, depending on bot complexity and detection scenario. The paper also describes the experiment design, collected datasets, statistical evaluation, and machine learning accuracy measures applied to support the results. In the discussion, we raise the question of using human labelling in bot detection systems and its potential cybersecurity issues. We also provide open access to the datasets used, experiment results, and software code for evaluating statistical and machine learning accuracy metrics used in this paper on GitHub. Full article
(This article belongs to the Special Issue Security, Privacy, and Trust in Artificial Intelligence Applications)
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15 pages, 571 KiB  
Article
X as a Passive Sensor to Identify Opinion Leaders: A Novel Method for Balancing Visibility and Community Engagement
by Marco Furini
Sensors 2024, 24(2), 610; https://doi.org/10.3390/s24020610 - 18 Jan 2024
Cited by 2 | Viewed by 1965
Abstract
The identification of opinion leaders is a matter of great significance for companies and authorities, as these individuals are able to shape the opinions and attitudes of entire societies. In this paper, we consider X (formerly Twitter) as a passive sensor to identify [...] Read more.
The identification of opinion leaders is a matter of great significance for companies and authorities, as these individuals are able to shape the opinions and attitudes of entire societies. In this paper, we consider X (formerly Twitter) as a passive sensor to identify opinion leaders. Given the unreliability of the traditional follower count metric due to the presence of fake accounts and farm bots, our approach combines the measures of visibility and community engagement to identify these influential individuals. Through an experimental evaluation involving approximately 4 million tweets, we showed two important findings: (i) relying solely on follower count or post frequency is inadequate for accurately identifying opinion leaders, (ii) opinion leaders are able to build community and gain visibility around specific themes. The results showed the benefits of using X as a passive sensor to identify opinion leaders, as the proposed method offers substantial advantages for those who are involved in social media communication strategies, including political campaigns, brand monitoring, and policymaking. Full article
(This article belongs to the Collection Sensors and Communications for the Social Good)
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16 pages, 1525 KiB  
Article
SqueezeGCN: Adaptive Neighborhood Aggregation with Squeeze Module for Twitter Bot Detection Based on GCN
by Chengqi Fu, Shuhao Shi, Yuxin Zhang, Yongmao Zhang, Jian Chen, Bin Yan and Kai Qiao
Electronics 2024, 13(1), 56; https://doi.org/10.3390/electronics13010056 - 21 Dec 2023
Cited by 2 | Viewed by 1642
Abstract
Despite notable advancements in bot detection methods based on Graph Neural Networks (GNNs). The efficacy of Graph Neural Networks relies heavily on the homophily assumption, which posits that nodes with the same label are more likely to form connections between them. However, the [...] Read more.
Despite notable advancements in bot detection methods based on Graph Neural Networks (GNNs). The efficacy of Graph Neural Networks relies heavily on the homophily assumption, which posits that nodes with the same label are more likely to form connections between them. However, the latest social bots are capable of concealing themselves by extensively interacting with authentic user accounts, forging extensive connections on social graphs, and thus deviating from the homophily assumption. Consequently, conventional Graph Neural Network methods continue to face significant challenges in detecting these novel types of social bots. To address this issue, we proposed SqueezeGCN, an adaptive neighborhood aggregation with the Squeeze Module for Twitter bot detection based on a GCN. The Squeeze Module uses a parallel multi-layer perceptron (MLP) to squeeze feature vectors into a one-dimensional representation. Subsequently, we adopted the sigmoid activation function, which normalizes values between 0 and 1, serving as node aggregation weights. The aggregation weight vector is processed by a linear layer to obtain the aggregation embedding, and the classification result is generated using a MLP classifier. This design generates adaptive aggregation weights for each node, diverging from the traditional singular neighbor aggregation approach. Our experiments demonstrate that SqueezeGCN performs well on three widely acknowledged Twitter bot detection benchmarks. Comparisons with a GCN reveal improvements of 2.37%, 15.59%, and 1.33% for the respective datasets. Furthermore, our approach demonstrates improvements when compared to state-of-the-art algorithms on the three benchmark datasets. The experimental results further affirm the exceptional effectiveness of our proposed algorithm for Twitter bot detection. Full article
(This article belongs to the Section Artificial Intelligence)
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