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Retweeting is an important way of information propagation on Twitter. In this paper, we investigate the sentiment correlation between regular tweets and retweets. We anticipate our investigation sheds a light on how the sentiment of regular tweets impacts the retweets of different sentiments. We propose a method for measuring the sentiment of tweets. We categorize the Twitter users into different groups by different norms, which are the follower count, the betweenness connectivity, a combination of follower count and betweenness centrality,and the amount of tweets. Then, we calculate the sentiment correlation for different groups to examine the influential factors for retweeting a message with a certain sentiment.We find that the users with higher betweenness centrality and higher tweets amount tend to exhibit a higher sentiment correlation. The users with medium-level followers_count show the highest sentiment correlation compared to the low-level and high-level followers_count. After combining the two factors of followers_count and betweenness centrality, we discover that specifically at low-level betweenness centrality the users with medium-level followers_count have the highest sentiment correlation. Our last observation is that the difference for correlation coefficients exists between different types of users. Our study on the sentiment correlation provides instructional information for modeling information propagation in human society. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature.
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Capacitive swipe gestures have recently emerged as a promising behavioral biometric modality for user authentication and identification on smartphones. While prior studies have demonstrated their effectiveness using classical machine learning techniques, the potential of deep learning in this domain remains largely unexplored, particularly under limited training data conditions. In this paper, we investigate the use of deep learning for capacitive swipe–based user identification and address the challenge of data scarcity through an image fusion strategy. Experiments on a dataset of 30 users show that the proposed "deep learning plus image fusion" approach significantly improves performance over a standalone deep learning baseline and achieves results comparable to state-of-the-art classical machine learning methods. These findings highlight the importance of data size and demonstrate the potential of image fusion for enhancing deep learning–based user identification.
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Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs) are widely used mechanisms for protecting websites against automated bot attacks. In this paper, we investigate the trade-off between usability and security in CAPTCHA design using a Stackelberg game-theoretic framework. Based on empirical evaluation data on CAPTCHA solving time and accuracy, we incorporate expected solving time into the defender's utility function to jointly capture usability and security. We propose two models: a discrete-strategy game, in which different CAPTCHA types and difficulty levels form the strategy space, and a continuous-strategy game, in which CAPTCHA hardness is represented as a continuous decision variable. For the discrete model, we derive a stepwise solution characterizing transitions in the defender's optimal strategy. For the continuous model, we analyze a threshold-based attacker response and obtain equilibrium solutions under deterrence and accommodation regimes. The proposed framework is adaptable to other CAPTCHA evaluation datasets.
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In online social networks (OSN), followers count is a sign of the social influence of an account. Some users expect to increase the followers count by following more accounts. However, in reality more followings do not generate more followers. In this paper, we propose a two player follow-unfollow game model and then introduce a factor for promoting cooperation. Based on the two player follow-unfollow game, we create an evolutionary follow-unfollow game with more players to simulate a miniature social network. We design an algorithm and conduct the simulation. From the simulation, we find that our algorithm for the evolutionary follow-unfollow game is able to converge and produce a stable network. Results obtained with different values of the cooperation promotion factor show that the promotion factor increases the total connections in the network especially through increasing the number of the follow follow connections.
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