Proceedings of the 2017 ACM on Web Science Conference最新文献

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Mean Birds: Detecting Aggression and Bullying on Twitter 刻薄的鸟:在Twitter上检测侵略和欺凌
Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-02-22 DOI: 10.1145/3091478.3091487
Despoina Chatzakou, N. Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, G. Stringhini, A. Vakali
{"title":"Mean Birds: Detecting Aggression and Bullying on Twitter","authors":"Despoina Chatzakou, N. Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, G. Stringhini, A. Vakali","doi":"10.1145/3091478.3091487","DOIUrl":"https://doi.org/10.1145/3091478.3091487","url":null,"abstract":"In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125266709","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: 363
Sharing Means Renting?: An Entire-marketplace Analysis of Airbnb 共享意味着租用?: Airbnb的整体市场分析
Proceedings of the 2017 ACM on Web Science Conference Pub Date : 2017-01-06 DOI: 10.1145/3091478.3091504
Qing Ke
{"title":"Sharing Means Renting?: An Entire-marketplace Analysis of Airbnb","authors":"Qing Ke","doi":"10.1145/3091478.3091504","DOIUrl":"https://doi.org/10.1145/3091478.3091504","url":null,"abstract":"Airbnb, an online marketplace for accommodations, has experienced a staggering growth accompanied by intense debates and scattered regulations around the world. Current discourses, however, are largely focused on opinions rather than empirical evidences. Here, we aim to bridge this gap by presenting the first large-scale measurement study on Airbnb, using a crawled data set containing 2.3 million listings, 1.3 million hosts, and 19.3 million reviews. We measure several key characteristics at the heart of the ongoing debate and the sharing economy. Among others, we find that Airbnb has reached a global yet heterogeneous coverage. The majority of its listings across many countries are entire homes, suggesting that Airbnb is actually more like a rental marketplace rather than a spare-room sharing platform. Analysis on star-ratings reveals that there is a bias toward positive ratings, amplified by a bias toward using positive words in reviews. The extent of such bias is greater than Yelp reviews, which were already shown to exhibit a positive bias. We investigate a key issue - commercial hosts who own multiple listings on Airbnb - repeatedly discussed in the current debate. We find that their existence is prevalent, they are early movers towards joining Airbnb, and their listings are disproportionately entire homes and located in the US. Our work advances the current understanding of how Airbnb is being used and may serve as an independent and empirical reference to inform the debate.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117059141","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: 63
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