2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第10页

Feature extraction and analysis for identifying disruptive events from social media 从社交媒体中识别破坏性事件的特征提取和分析

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808867

Nasser Alsaedi, P. Burnap

{"title":"Feature extraction and analysis for identifying disruptive events from social media","authors":"Nasser Alsaedi, P. Burnap","doi":"10.1145/2808797.2808867","DOIUrl":"https://doi.org/10.1145/2808797.2808867","url":null,"abstract":"Disruptive event identification is a concept that is crucial to ensuring public safety regarding large-scale events. Recent work on detecting events from social media shows that although these platforms are used for social purposes, they have been emerging as important source of information. Twitter, as a form of social media, is a popular micro-blogging web application serving hundreds of millions of users. User-generated content can be exploited as a rich source of information for identifying `real-world' disruptive events - events that threaten social safety and security, or could cause disruption to social order. In this paper, we present an in-depth comparison of two types of feature that could be useful for identifying disruptive events: temporal and textual features. On the basis of these features, we investigate the dynamics of event/topic identification over time. We make several interesting observations: first, disruptive events are identifiable regardless of the \"influence of the user\" discussing them, and over a variety of topics. Second, temporal features play a central role in event detection and hence should not be disregarded or ignored. Third, textual features can be used to improve the overall performance of the event detection. We believe that these findings provide new insights for gathering information around real-world events, in particular for detecting disruptive events.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129126793","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: 11

Graph-based term weighting for text categorization 用于文本分类的基于图的术语加权

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808872

Fragkiskos D. Malliaros, Konstantinos Skianis

引用次数: 49

Investigating the types and effects of missing data in multilayer networks 研究多层网络中丢失数据的类型和影响

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808889

Rajesh Sharma, Matteo Magnani, D. Montesi

引用次数: 12

Weak signals as predictors of real-world phenomena in social media 社交媒体中作为现实世界现象预测者的微弱信号

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809332

C. Charitonidis, A. Rashid, Paul J. Taylor

{"title":"Weak signals as predictors of real-world phenomena in social media","authors":"C. Charitonidis, A. Rashid, Paul J. Taylor","doi":"10.1145/2808797.2809332","DOIUrl":"https://doi.org/10.1145/2808797.2809332","url":null,"abstract":"Global and national events in recent years have shown that online social media can be a force for good (e.g., Arab Spring) and harm (e.g., the London riots). In both of these examples, social media played a key role in group formation and organization, and in the coordination of the group's subsequent collective actions (i.e., the move from rhetoric to action). Surprisingly, despite its clear importance, little is understood about the factors that lead to this kind of group development and the transition to collective action. This paper focuses on an approach to the analysis of data from social media to detect weak signals, i.e., indicators that initially appear at the fringes, but are, in fact, early indicators of such large-scale real-world phenomena. Our approach is in contrast to existing research which focuses on analysing major themes, i.e., the strong signals, prevalent in a social network at a particular point in time. Analysis of weak signals can provide interesting possibilities for forecasting, with online user-generated content being used to identify and anticipate possible offline future events. We demonstrate our approach through analysis of tweets collected during the London riots in 2011 and use of our weak signals to predict tipping points in that context.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115201161","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: 10

Trust inference in online social networks 在线社交网络中的信任推理

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809418

A. Papaoikonomou, Magdalini Kardara, T. Varvarigou

引用次数: 7

Birds of a feather flock together: The accidental communities of spammers 物以类聚:垃圾邮件制造者的偶然群体

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808843

Yehonatan Cohen, Danny Hendler

引用次数: 1

Near linear-time community detection in networks with hardly detectable community structure 社团结构难以检测的网络中的近线性时间社团检测

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808903

A. Rezaei, Saeed Mahlouji Far, Mahdieh Soleymani

引用次数: 5

On the skewed degree distribution of hierarchical networks 关于层次网络的偏斜度分布

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809409

B. R. Sahraei, Haitham Bou-Ammar, K. Tuyls, Gerhard Weiss

引用次数: 0

Importance of data mining in healthcare: A survey 数据挖掘在医疗保健中的重要性:一项调查

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809367

Mohammad Hossein Tekieh, B. Raahemi

引用次数: 44

Combining propensity and influence models for product adoption prediction 结合倾向模型和影响模型进行产品采用率预测

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808851

I. Verenich, R. Kikas, M. Dumas, D. Melnikov

引用次数: 1