Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining最新文献

An ensemble transformer-based model for Arabic sentiment analysis 基于集成变换的阿拉伯语情感分析模型

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-12-24 DOI: 10.1007/s13278-022-01009-0

Omar Mohamed, Aly M. Kassem, Ali Ashraf, Salma Jamal, E. Mohamed

引用次数: 4

Homophily and polarization on political twitter during the 2017 Norwegian election 2017年挪威大选期间政治推特上的同质性和两极分化

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-12-23 DOI: 10.1007/s13278-022-01018-z

B. Enjolras, A. Salway

引用次数: 2

Perceptible sentiment analysis of students' WhatsApp group chats in valence, arousal, and dominance space 学生WhatsApp群聊在效价、唤醒和支配空间的感知情感分析

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-12-19 DOI: 10.1007/s13278-022-01016-1

B. Roy, Sourav Das

引用次数: 2

A performant deep learning model for sentiment analysis of climate change 一种用于气候变化情感分析的高性能深度学习模型

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-12-15 DOI: 10.1007/s13278-022-01014-3

Mustapha Lydiri, Yousef El Mourabit, Y. E. Habouz, Mohamed Fakir

引用次数: 4

DEES: a real-time system for event extraction from disaster-related web text 从与灾害相关的网络文本中提取事件的实时系统

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-12-11 DOI: 10.1007/s13278-022-01007-2

N. Algiriyage, R. Prasanna, Kristin Stock, Emma E. H. Doyle, David Johnston

引用次数: 1

Link prediction using betweenness centrality and graph neural networks 使用中间性中心性和图神经网络的链接预测

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-12-10 DOI: 10.1007/s13278-022-00999-1

Ayoub Jibouni, D. Lotfi, A. Hammouch

引用次数: 3

Finding Early Adopters of Innovation in Social Network 寻找社交网络创新的早期采用者

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-10-25 DOI: 10.48550/arXiv.2210.13907

B. Sziklai, B. Lengyel

{"title":"Finding Early Adopters of Innovation in Social Network","authors":"B. Sziklai, B. Lengyel","doi":"10.48550/arXiv.2210.13907","DOIUrl":"https://doi.org/10.48550/arXiv.2210.13907","url":null,"abstract":"Social networks play a fundamental role in the diffusion of innovation through peers’ influence on adoption. Thus, network position including a wide range of network centrality measures has been used to describe individuals’ affinity to adopt an innovation and their ability to propagate diffusion. Yet, social networks are assortative in terms of susceptibility and influence and in terms of network centralities as well. This makes the identification of influencers difficult especially since susceptibility and centrality do not always go hand in hand. Here, we propose the Top Candidate algorithm, an expert recommendation method, to rank individuals based on their perceived expertise, which resonates well with the assortative mixing of innovators and early adopters in networks. Leveraging adoption data from two online social networks that are assortative in terms of adoption but represent different levels of assortativity of network centralities, we demonstrate that the Top Candidate ranking is more efficient in capturing innovators and early adopters than other widely used indices. Top Candidate nodes adopt earlier and have higher reach among innovators, early adopters and early majority than nodes highlighted by other methods. These results suggest that the Top Candidate method can identify good seeds for influence maximization campaigns on social networks.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"1 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88439603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survival analysis for user disengagement prediction: question-and-answering communities' case 用户脱离预测的生存分析:问答社区案例

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2022-03-29 DOI: 10.48550/arXiv.2203.15255

H. A. Firouzjaei

{"title":"Survival analysis for user disengagement prediction: question-and-answering communities' case","authors":"H. A. Firouzjaei","doi":"10.48550/arXiv.2203.15255","DOIUrl":"https://doi.org/10.48550/arXiv.2203.15255","url":null,"abstract":"We used survival analysis to model user disengagement in three distinct questions-and-answering communities in this work. We used the complete historical data of {Politics, Data Science, Computer Science} Stack Exchange communities from their inception until May 2021, which include the information about all users who were members of one of these three communities. Furthermore, formulating the user disengagement prediction as a survival analysis task, we utilised two survival analysis techniques to model and predict the probabilities of members of each community becoming disengaged. Our main finding is that the likelihood of users with even a few contributions staying active is noticeably higher than the users who were making no contributions; this distinction may widen as time passes. Moreover, the results of our experiments indicate that users with more favourable views towards the content shared on the platform may stay engaged longer. Finally, the observed pattern holds for all three communities, regardless of their themes.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"28 1","pages":"86"},"PeriodicalIF":0.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86942926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Mapping the Complexity of Suicide by Combining Participatory Modeling and Network Science. 参与式模型与网络科学相结合，映射自杀的复杂性。

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488271

Philippe J Giabbanelli, Michael C Galgoczy, Duc M Nguyen, Romain Foy, Ketra L Rice, Nisha Nataraj, Margaret M Brown, Christopher R Harper

{"title":"Mapping the Complexity of Suicide by Combining Participatory Modeling and Network Science.","authors":"Philippe J Giabbanelli, Michael C Galgoczy, Duc M Nguyen, Romain Foy, Ketra L Rice, Nisha Nataraj, Margaret M Brown, Christopher R Harper","doi":"10.1145/3487351.3488271","DOIUrl":"https://doi.org/10.1145/3487351.3488271","url":null,"abstract":"<p><p>Suicide rates are steadily increasing among youth in the USA. Although several theories and frameworks of suicide have been developed, they do not account for some of the features that define suicide as a complex problem, such as a large number of interrelationships and cycles. In this paper, we create the first c omprehensive m ap o f a dverse c hildhood experiences (ACEs) and suicide for youth, by combining a participatory approach (involving 15 subject-matter experts) and network science. This results in a map of 946 edges and 361 concepts, in which we identify ACEs to be the most important factor (per degree centrality). The map is openly shared with the community to support further network analyses (e.g., decomposition into clusters). Similarly to the high-impact Foresight Map developed in the context of obesity, the largest map on suicide and ACEs to date presented in this paper can start a discussion at the crossroad of suicide research and network science, thus bringing new means to address a complex public health challenge.</p>","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"12 1","pages":"339-342"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10194413/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9510506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Optimal control of a delayed rumor propagation model with saturated control functions and L1-type objectives 具有饱和控制函数和l1型目标的延迟谣言传播模型的最优控制

Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining Pub Date : 2020-08-26 DOI: 10.1007/s13278-020-00685-0

A. Abta, H. Laarabi, M. Rachik, H. Alaoui, Salahaddine Boutayeb

引用次数: 4