I.5. Social and Behavioral Sciences
Havva Alizadeh Noughabi
Abstract
Social media platforms have transformed information consumption, offering personalized features that enhance engagement and streamline content discovery. Among these, the Twitter Lists functionality allows users to curate content by grouping accounts based on shared themes, fostering focused interactions ...
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Social media platforms have transformed information consumption, offering personalized features that enhance engagement and streamline content discovery. Among these, the Twitter Lists functionality allows users to curate content by grouping accounts based on shared themes, fostering focused interactions and diverse perspectives. Despite their widespread use, the relationship between user-generated content and List subscription behaviors remains insufficiently explored. This study examines the alignment between users' post topics and their subscribed Lists, along with the influence of activity levels on this alignment. The role of content diversity in shaping subscription patterns to Lists covering a range of topics is also investigated. Additionally, the extent to which the affective characteristics—sentiment and emotion—of user posts correspond with the emotional tone of subscribed List content is analyzed. Utilizing a comprehensive Twitter dataset, advanced techniques for topic modeling, sentiment analysis, and emotion extraction were applied, and profiles for both users and Lists were developed to facilitate the exploration of their interrelationship. These insights advance the understanding of user interactions with Lists, informing the development of personalized recommendation systems and improved content curation strategies, with broad implications for social media research and platform functionality.
H.3. Artificial Intelligence
Akram Pasandideh; Mohsen Jahanshahi
Abstract
Link prediction (LP) has become a hot topic in the data mining, machine learning, and deep learning community. This study aims to implement bibliometric analysis to find the current status of the LP studies and investigate it from different perspectives. The present study provides a Scopus-based bibliometric ...
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Link prediction (LP) has become a hot topic in the data mining, machine learning, and deep learning community. This study aims to implement bibliometric analysis to find the current status of the LP studies and investigate it from different perspectives. The present study provides a Scopus-based bibliometric overview of the LP studies landscape since 1987 when LP studies were published for the first time. Various kinds of analysis, including document, subject, and country distribution are applied. Moreover, author productivity, citation analysis, and keyword analysis is used, and Bradford’s law is applied to discover the main journals in this field. Most documents were published by conferences in the field. The majority of LP documents have been published in the computer science and mathematics fields. So far, China has been at the forefront of publishing countries. In addition, the most active sources of LP publications are lecture notes in Computer Science, including subseries lecture notes in Artificial Intelligence (AI) and lecture notes in Bioinformatics, and IEEE Access. The keyword analysis demonstrates that while social networks had attracted attention in the early period, knowledge graphs have attracted more attention, recently. Since the LP problem has been approached recently using machine learning (ML), the current study may inform researchers to concentrate on ML techniques. This is the first bibliometric study of “link prediction” literature and provides a broad landscape of the field.
H.3. Artificial Intelligence
Z. Karimi Zandian; M. R. Keyvanpour
Abstract
Fraud detection is one of the ways to cope with damages associated with fraudulent activities that have become common due to the rapid development of the Internet and electronic business. There is a need to propose methods to detect fraud accurately and fast. To achieve to accuracy, fraud detection methods ...
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Fraud detection is one of the ways to cope with damages associated with fraudulent activities that have become common due to the rapid development of the Internet and electronic business. There is a need to propose methods to detect fraud accurately and fast. To achieve to accuracy, fraud detection methods need to consider both kind of features, features based on user level and features based on network level. In this paper a method called MEFUASN is proposed to extract features that is based on social network analysis and then both of obtained features and features based on user level are combined together and used to detect fraud using semi-supervised learning. Evaluation results show using the proposed feature extraction as a pre-processing step in fraud detection improves the accuracy of detection remarkably while it controls runtime in comparison with other methods.
