The proliferation of rumors on social media has caused serious harm to society.Although previous research has attempted to use deep learning methods for rumor detection,they did not simultaneously consider the two key...The proliferation of rumors on social media has caused serious harm to society.Although previous research has attempted to use deep learning methods for rumor detection,they did not simultaneously consider the two key features of temporal and spatial domains.More importantly,these methods struggle to automatically generate convincing explanations for the detection results,which is crucial for preventing the further spread of rumors.To address these limitations,this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models(LLMs)to automatically generate explanations for the detection results.Our method constructs a dynamic graph model to represent the evolving,tree-like propagation structure of rumors across different time periods.Spatial features are extracted using a Graph Convolutional Network,which captures the interactions and relationships between entities within the rumor network.Temporal features are extracted using a Recurrent Neural Network,which accounts for the dynamics of rumor spread over time.To automatically generate explanations,we utilize Llama-3-8B,a large language model,to provide clear and contextually relevant rationales for the detected rumors.We evaluate our method on two real-world datasets and demonstrate that it outperforms current state-of-the-art techniques,achieving superior detection accuracy while also offering the added capability of automatically generating interpretable and convincing explanations.Our results highlight the effectiveness of combining temporal and spatial features,along with LLMs,for improving rumor detection and understanding.展开更多
Rumor detection has become an emerging and active research field in recent years.At the core is to model the rumor characteristics inherent in rich information,such as propagation patterns in social network and semant...Rumor detection has become an emerging and active research field in recent years.At the core is to model the rumor characteristics inherent in rich information,such as propagation patterns in social network and semantic patterns in post content,and differentiate them from the truth.However,existing works on rumor detection fall short in modeling heterogeneous information,either using one single information source only(e.g.,social network,or post content)or ignoring the relations among multiple sources(e.g.,fusing social and content features via simple concatenation).Therefore,they possibly have drawbacks in comprehensively understanding the rumors,and detecting them accurately.In this work,we explore contrastive self-supervised learning on heterogeneous information sources,so as to reveal their relations and characterize rumors better.Technically,we supplement the main supervised task of detection with an auxiliary self-supervised task,which enriches post representations via post self-discrimination.Specifically,given two heterogeneous views of a post(i.e.,representations encoding social patterns and semantic patterns),the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts.We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination,considering different relations of information sources.We term this framework as self-supervised rumor detection(SRD).Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.展开更多
Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,espec...Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health crises.With huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been verified.During COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information.However,very few studies have been conducted for this purpose for the Arabic language,which has unique characteristics.Therefore,this paper proposes a comprehensive approach which includes two phases:detection and tracking.In the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the rumors.The experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.展开更多
基金supported by General Scientific Research Project of Zhejiang Provincial Department of Education(Y202353247).
文摘The proliferation of rumors on social media has caused serious harm to society.Although previous research has attempted to use deep learning methods for rumor detection,they did not simultaneously consider the two key features of temporal and spatial domains.More importantly,these methods struggle to automatically generate convincing explanations for the detection results,which is crucial for preventing the further spread of rumors.To address these limitations,this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models(LLMs)to automatically generate explanations for the detection results.Our method constructs a dynamic graph model to represent the evolving,tree-like propagation structure of rumors across different time periods.Spatial features are extracted using a Graph Convolutional Network,which captures the interactions and relationships between entities within the rumor network.Temporal features are extracted using a Recurrent Neural Network,which accounts for the dynamics of rumor spread over time.To automatically generate explanations,we utilize Llama-3-8B,a large language model,to provide clear and contextually relevant rationales for the detected rumors.We evaluate our method on two real-world datasets and demonstrate that it outperforms current state-of-the-art techniques,achieving superior detection accuracy while also offering the added capability of automatically generating interpretable and convincing explanations.Our results highlight the effectiveness of combining temporal and spatial features,along with LLMs,for improving rumor detection and understanding.
基金supported by the National Key Research and Development Program of China(2020AAA0106000)the National Natural Science Foundation of China(Grant Nos.U21B2026,62121002)the CCCD Key Lab of Ministry of Culture and Tourism.
文摘Rumor detection has become an emerging and active research field in recent years.At the core is to model the rumor characteristics inherent in rich information,such as propagation patterns in social network and semantic patterns in post content,and differentiate them from the truth.However,existing works on rumor detection fall short in modeling heterogeneous information,either using one single information source only(e.g.,social network,or post content)or ignoring the relations among multiple sources(e.g.,fusing social and content features via simple concatenation).Therefore,they possibly have drawbacks in comprehensively understanding the rumors,and detecting them accurately.In this work,we explore contrastive self-supervised learning on heterogeneous information sources,so as to reveal their relations and characterize rumors better.Technically,we supplement the main supervised task of detection with an auxiliary self-supervised task,which enriches post representations via post self-discrimination.Specifically,given two heterogeneous views of a post(i.e.,representations encoding social patterns and semantic patterns),the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts.We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination,considering different relations of information sources.We term this framework as self-supervised rumor detection(SRD).Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
基金This research was funded by the Deanship of Scientific Research,Imam Mohammad Ibn Saud Islamic University,Saudi Arabia,Grant No.(20-12-18-013).
文摘Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health crises.With huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been verified.During COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social media.Several methods have been applied for detecting rumors and tracking their sources for COVID 19-related information.However,very few studies have been conducted for this purpose for the Arabic language,which has unique characteristics.Therefore,this paper proposes a comprehensive approach which includes two phases:detection and tracking.In the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the rumors.The experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.
