Sabrina Sicari, Jesus F. Cevallos M., Alessandra Rizzardi, Alberto Coen-Porisini
This survey summarizes the most recent methods for building and assessing helpful, honest, and harmless neural language models, considering small, medium, and large-size models. Pointers to open-source resources that help to align pre-trained models are given, including methods that use parameter-efficient techniques, specialized prompting frameworks, adapter modules, case-specific knowledge injection, and adversarially robust training techniques. Special care is given to evidencing recent progress on value alignment, commonsense reasoning, factuality enhancement, and abstract reasoning of language models. Most reviewed works in this survey publicly shared their code and related data and were accepted in world-leading Machine Learning venues. This work aims to help researchers and practitioners accelerate their entrance into the field of human-centric neural language models, which might be a cornerstone of the contemporary and near-future industrial and societal revolution.
{"title":"Open-Ethical AI: Advancements in Open-Source Human-Centric Neural Language Models","authors":"Sabrina Sicari, Jesus F. Cevallos M., Alessandra Rizzardi, Alberto Coen-Porisini","doi":"10.1145/3703454","DOIUrl":"https://doi.org/10.1145/3703454","url":null,"abstract":"This survey summarizes the most recent methods for building and assessing <jats:italic>helpful, honest, and harmless</jats:italic> neural language models, considering small, medium, and large-size models. Pointers to open-source resources that help to align pre-trained models are given, including methods that use parameter-efficient techniques, specialized prompting frameworks, adapter modules, case-specific knowledge injection, and adversarially robust training techniques. Special care is given to evidencing recent progress on value alignment, commonsense reasoning, factuality enhancement, and abstract reasoning of language models. Most reviewed works in this survey publicly shared their code and related data and were accepted in world-leading Machine Learning venues. This work aims to help researchers and practitioners accelerate their entrance into the field of human-centric neural language models, which might be a cornerstone of the contemporary and near-future industrial and societal revolution.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shamsher Ullah, Jianqiang Li, Jie Chen, IKRAM ALI, Salabat Khan, Abdul Ahad, Farhan Ullah, Victor Leung
A delay, interruption, or failure in the wireless connection has a significant impact on the performance of wirelessly connected medical equipment. Researchers presented the fastest technological innovations and industrial changes to address these problems and improve the applications of information and communication technology. The development of the 6G communication infrastructure was greatly aided by the use of Block-chain technology, artificial intelligence (AI), virtual reality (VR), and the Internet of Things (IoT). In this paper, we comprehensively discuss 6G technologies enhancement, its fundamental architecture, difficulties, and other issues associated with it. In addition, the outcomes of our research help make 6G technology more applicable to real-world medical environments. The most important thing that this study has contributed is an explanation of the path that future research will take and the current state of the art. This study might serve as a jumping-off point for future researchers in the academic world who are interested in investigating the possibilities of 6G technological developments.
{"title":"A Survey on Emerging Trends and Applications of 5G and 6G to Healthcare Environments","authors":"Shamsher Ullah, Jianqiang Li, Jie Chen, IKRAM ALI, Salabat Khan, Abdul Ahad, Farhan Ullah, Victor Leung","doi":"10.1145/3703154","DOIUrl":"https://doi.org/10.1145/3703154","url":null,"abstract":"A delay, interruption, or failure in the wireless connection has a significant impact on the performance of wirelessly connected medical equipment. Researchers presented the fastest technological innovations and industrial changes to address these problems and improve the applications of information and communication technology. The development of the 6G communication infrastructure was greatly aided by the use of Block-chain technology, artificial intelligence (AI), virtual reality (VR), and the Internet of Things (IoT). In this paper, we comprehensively discuss 6G technologies enhancement, its fundamental architecture, difficulties, and other issues associated with it. In addition, the outcomes of our research help make 6G technology more applicable to real-world medical environments. The most important thing that this study has contributed is an explanation of the path that future research will take and the current state of the art. This study might serve as a jumping-off point for future researchers in the academic world who are interested in investigating the possibilities of 6G technological developments.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"26 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bjorn De Sutter, Sebastian Schrittwieser, Bart Coppens, Patrick Kochberger
Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 571 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks and formulate a number of concrete recommendations for improving the evaluations reported in future research papers.
{"title":"Evaluation Methodologies in Software Protection Research","authors":"Bjorn De Sutter, Sebastian Schrittwieser, Bart Coppens, Patrick Kochberger","doi":"10.1145/3702314","DOIUrl":"https://doi.org/10.1145/3702314","url":null,"abstract":"<jats:italic>Man-at-the-end</jats:italic> (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 571 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks and formulate a number of concrete recommendations for improving the evaluations reported in future research papers.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"7 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marion Bartl, Abhishek Mandal, Susan Leavy, Suzanne Little
Taking an interdisciplinary approach to surveying issues around gender bias in textual and visual AI, we present literature on gender bias detection and mitigation in NLP, CV, as well as combined visual-linguistic models. We identify conceptual parallels between these strands of research as well as how methodologies were adapted cross-disciplinary from NLP to CV. We also find that there is a growing awareness for theoretical frameworks from the social sciences around gender in NLP that could be beneficial for aligning bias analytics in CV with human values and conceptualising gender beyond the binary categories of male/female.
{"title":"Gender Bias in Natural Language Processing and Computer Vision: A Comparative Survey","authors":"Marion Bartl, Abhishek Mandal, Susan Leavy, Suzanne Little","doi":"10.1145/3700438","DOIUrl":"https://doi.org/10.1145/3700438","url":null,"abstract":"Taking an interdisciplinary approach to surveying issues around gender bias in textual and visual AI, we present literature on gender bias detection and mitigation in NLP, CV, as well as combined visual-linguistic models. We identify conceptual parallels between these strands of research as well as how methodologies were adapted cross-disciplinary from NLP to CV. We also find that there is a growing awareness for theoretical frameworks from the social sciences around gender in NLP that could be beneficial for aligning bias analytics in CV with human values and conceptualising gender beyond the binary categories of male/female.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"145 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Researchers’ interest in Fog Computing and its application in different sectors has been increasing since the last decade. To discover the emerging trends inherent to this architecture, we analyzed the scientific literature indexed in Scopus through a bibliometric study. Exposing trends in areas of development will allow researchers to understand the changes and evolution over time. For analysis purposes, we used three approaches: performance analysis, science mapping, and literature clustering. Analysis results revealed promising investigation areas in the Fog Computing architecture from 2012 to 2021, which emphasizes that Fog Computing will continue to be an interesting field of research in the future.
{"title":"Fog Computing Technology Research: A Retrospective Overview and Bibliometric Analysis","authors":"Paola Vinueza-Naranjo, Janneth Chicaiza, Ruben Rumipamba-Zambrano","doi":"10.1145/3702313","DOIUrl":"https://doi.org/10.1145/3702313","url":null,"abstract":"Researchers’ interest in Fog Computing and its application in different sectors has been increasing since the last decade. To discover the emerging trends inherent to this architecture, we analyzed the scientific literature indexed in Scopus through a bibliometric study. Exposing trends in areas of development will allow researchers to understand the changes and evolution over time. For analysis purposes, we used three approaches: performance analysis, science mapping, and literature clustering. Analysis results revealed promising investigation areas in the Fog Computing architecture from 2012 to 2021, which emphasizes that Fog Computing will continue to be an interesting field of research in the future.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"45 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition, we present a comprehensive survey on recent advances in adversarial attacks and defenses for medical image analysis with a systematic taxonomy in terms of the application scenario. We also provide a unified framework for different types of adversarial attack and defense methods in the context of medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models obtained by adversarial training under various scenarios. To the best of our knowledge, this is the first survey paper that provides a thorough evaluation of adversarially robust medical diagnosis models. By analyzing qualitative and quantitative results, we conclude this survey with a detailed discussion of current challenges for adversarial attack and defense in medical image analysis systems to shed light on future research directions. Code is available on GitHub.
{"title":"Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges","authors":"Junhao Dong, Junxi Chen, Xiaohua Xie, Jianhuang Lai, Hao Chen","doi":"10.1145/3702638","DOIUrl":"https://doi.org/10.1145/3702638","url":null,"abstract":"Deep learning techniques have achieved superior performance in computer-aided medical image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in potential misdiagnosis in clinical practice. Oppositely, recent years have also witnessed remarkable progress in defense against these tailored adversarial examples in deep medical diagnosis systems. In this exposition, we present a comprehensive survey on recent advances in adversarial attacks and defenses for medical image analysis with a systematic taxonomy in terms of the application scenario. We also provide a unified framework for different types of adversarial attack and defense methods in the context of medical image analysis. For a fair comparison, we establish a new benchmark for adversarially robust medical diagnosis models obtained by adversarial training under various scenarios. To the best of our knowledge, this is the first survey paper that provides a thorough evaluation of adversarially robust medical diagnosis models. By analyzing qualitative and quantitative results, we conclude this survey with a detailed discussion of current challenges for adversarial attack and defense in medical image analysis systems to shed light on future research directions. Code is available on GitHub.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"51 2 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142556041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric authentication. However, prior research has revealed the vulnerability of VRSs to backdoor attacks, which pose a significant threat to the security and privacy of VRSs. Unfortunately, existing literature lacks a thorough review on this topic. This paper fills this research gap by conducting a comprehensive survey on backdoor attacks against VRSs. We first present an overview of VRSs and backdoor attacks, elucidating their basic knowledge. Then we propose a set of evaluation criteria to assess the performance of backdoor attack methods. Next, we present a comprehensive taxonomy of backdoor attacks against VRSs from different perspectives and analyze the characteristic of different categories. After that, we comprehensively review existing attack methods and analyze their pros and cons based on the proposed criteria. Furthermore, we review classic backdoor defense methods and generic audio defense techniques. Then we discuss the feasibility of deploying them on VRSs. Finally, we figure out several open issues and further suggest future research directions to motivate the research of VRSs security.
{"title":"Backdoor Attacks against Voice Recognition Systems: A Survey","authors":"Baochen Yan, Jiahe Lan, Zheng Yan","doi":"10.1145/3701985","DOIUrl":"https://doi.org/10.1145/3701985","url":null,"abstract":"Voice Recognition Systems (VRSs) employ deep learning for speech recognition and speaker recognition. They have been widely deployed in various real-world applications, from intelligent voice assistance to telephony surveillance and biometric authentication. However, prior research has revealed the vulnerability of VRSs to backdoor attacks, which pose a significant threat to the security and privacy of VRSs. Unfortunately, existing literature lacks a thorough review on this topic. This paper fills this research gap by conducting a comprehensive survey on backdoor attacks against VRSs. We first present an overview of VRSs and backdoor attacks, elucidating their basic knowledge. Then we propose a set of evaluation criteria to assess the performance of backdoor attack methods. Next, we present a comprehensive taxonomy of backdoor attacks against VRSs from different perspectives and analyze the characteristic of different categories. After that, we comprehensively review existing attack methods and analyze their pros and cons based on the proposed criteria. Furthermore, we review classic backdoor defense methods and generic audio defense techniques. Then we discuss the feasibility of deploying them on VRSs. Finally, we figure out several open issues and further suggest future research directions to motivate the research of VRSs security.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.
{"title":"Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning","authors":"Aulia Arif Wardana, Parman Sukarno","doi":"10.1145/3701724","DOIUrl":"https://doi.org/10.1145/3701724","url":null,"abstract":"This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"3 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa, Guido Perboli
Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance complexity, and some security related issues. These limitations prevent its adoption in mainstream applications. Artificial Intelligence (AI) can help addressing some of these limitations. This survey provides a detailed overview of the different blockchain AI-based optimization and improvement approaches, tools and methodologies proposed to meet the needs of existing systems and applications with their benefits and drawbacks. Afterwards, the focus is on suggesting AI-based directions where to address some of the fundamental limitations of blockchain.
{"title":"A Review on Blockchain Technology, Current Challenges, and AI-Driven Solutions","authors":"Moetez Abdelhamid, Layth Sliman, Raoudha Ben Djemaa, Guido Perboli","doi":"10.1145/3700641","DOIUrl":"https://doi.org/10.1145/3700641","url":null,"abstract":"Blockchain provides several advantages, including decentralization, data integrity, traceability, and immutability. However, despite its advantages, blockchain suffers from significant limitations, including scalability, resource greediness, governance complexity, and some security related issues. These limitations prevent its adoption in mainstream applications. Artificial Intelligence (AI) can help addressing some of these limitations. This survey provides a detailed overview of the different blockchain AI-based optimization and improvement approaches, tools and methodologies proposed to meet the needs of existing systems and applications with their benefits and drawbacks. Afterwards, the focus is on suggesting AI-based directions where to address some of the fundamental limitations of blockchain.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed Mostafa, Ahmad S Almogren, Muhammad Al-Qurishi, Majed Alrubaian
Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.
{"title":"Modality deep-learning frameworks for fake news detection on social networks: a systematic literature review","authors":"Mohamed Mostafa, Ahmad S Almogren, Muhammad Al-Qurishi, Majed Alrubaian","doi":"10.1145/3700748","DOIUrl":"https://doi.org/10.1145/3700748","url":null,"abstract":"Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"13 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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