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detecting anomalous user behavior in online social networks","{\"openalex\":\"W2218242280\"}","2026-05-23T04:45:16.20205061","2026-05-23T04:45:16.202051741","doi","arxivId","openalexId","W2218242280","abstractText","Users increasingly rely on crowdsourced information, such as reviews on Yelp and Amazon, and liked posts and ads on Facebook. This has led to a market for blackhat promotion techniques via fake (e.g., Sybil) and compromised accounts, and collusion networks. Existing approaches to detect such behavior relies mostly on supervised (or semi-supervised) learning over known (or hypothesized) attacks. They are unable to detect attacks missed by the operator while labeling, or when the attacker changes strategy. We propose using unsupervised anomaly detection techniques over user behavior to distinguish potentially bad behavior from normal behavior. We present a technique based on Principal Component Analysis (PCA) that models the behavior of normal users accurately and identifies significant deviations from it as anomalous. We experimentally validate that normal user behavior (e.g., categories of Facebook pages liked by a user, rate of like activity, etc.) is contained within a low-dimensional subspace amenable to the PCA technique. We demonstrate the practicality and effectiveness of our approach using extensive ground-truth data from Facebook: we successfully detect diverse attacker strategies—fake, compromised, and colluding Facebook identities—with no apriori labeling while maintaining low false-positive rates. Finally, we apply our approach to detect click-spam in Facebook ads and find that a surprisingly large fraction of clicks are from anomalous users. 1","publishedDate","2014-08-20","publishedYear",2014,"venue",225,"authors","keywords",[167,168,169],"pdfUrl","topVenue","Spam and Phishing Detection","Network Security and Intrusion Detection","Internet Traffic Analysis and Secure E-voting","paper_W2340135383","An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace","{\"openalex\":\"W2340135383\",\"doi\":\"10.1145/2872427.2883089\"}","2026-05-21T16:17:18.193974465","2026-05-21T16:24:47.183041585","10.1145/2872427.2883089","W2340135383","The rise of e-commerce has unlocked practical applications for algorithmic pricing (also called dynamic pricing algorithms), where sellers set prices using computer algorithms. Travel websites and large, well known e-retailers have already adopted algorithmic pricing strategies, but the tools and techniques are now available to small-scale sellers as well.","2016-04-11",2016,283,[182,183,184],"Consumer Market Behavior and Pricing","Blockchain Technology Applications and Security","Auction Theory and Applications","paper_W2153644028","An analysis of social network-based Sybil defenses","{\"openalex\":\"W2153644028\",\"doi\":\"10.1145/1851182.1851226\"}","2026-05-20T21:57:01.230779085","2026-05-20T21:57:01.23085075","10.1145/1851182.1851226","W2153644028","Recently, there has been much excitement in the research community over using social networks to mitigate multiple identity, or Sybil, attacks. A number of schemes have been proposed, but they differ greatly in the algorithms they use and in the networks upon which they are evaluated. As a result, the research community lacks a clear understanding of how these schemes compare against each other, how well they would work on real-world social networks with different structural properties, or whether there exist other (potentially better) ways of Sybil defense.","2010-08-30",2010,314,[167,168,198],"https://dl.acm.org/doi/pdf/10.1145/1851182.1851226","Advanced Malware Detection Techniques","paper_W2109882182","Measuring personalization of web search","{\"openalex\":\"W2109882182\",\"doi\":\"10.1145/2488388.2488435\"}","2026-05-19T17:14:40.217276888","2026-05-19T17:14:40.238154812","10.1145/2488388.2488435","W2109882182","Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it.","2013-05-13",2013,393,[211,212,213],"Information Retrieval and Search Behavior","Web Data Mining and Analysis","Recommender Systems and Techniques","paper_W2118994807","Analyzing facebook privacy settings","{\"openalex\":\"W2118994807\",\"doi\":\"10.1145/2068816.2068823\"}","2026-05-16T11:31:50.210859044","2026-05-19T05:36:02.183542027","10.1145/2068816.2068823","W2118994807","The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide better tools for managing privacy, but we are limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of the incidence of incorrect privacy settings or the difficulty users face when managing their privacy.","2011-11-02",2011,459,[226,227,169],"Privacy, Security, and Data Protection","Privacy-Preserving Technologies in Data","paper_W3106003309","","{\"openalex\":\"W3106003309\"}","2026-05-15T03:07:17.37194392","2026-05-21T02:07:42.184307509","W3106003309","NLP tasks are often limited by scarcity of
manually annotated data. In social media
sentiment analysis and related tasks,
researchers have therefore used binarized
emoticons and specific hashtags as forms
of distant supervision. Our paper shows
that by extending the distant supervision
to a more diverse set of noisy labels, the
models can learn richer representations.
Through emoji prediction on a dataset of
1246 million tweets containing one of 64
common emojis we obtain state-of-theart
performance on 8 benchmark datasets
within emotion, sentiment and sarcasm detection
using a single pretrained model.
Our analyses confirm that the diversity of
our emotional labels yield a performance
improvement over previous distant supervision
approaches.","2017-01-01",2017,"Research at the University of Copenhagen (University of Copenhagen)",732,[240,241,242],"Sentiment Analysis and Opinion Mining","Topic Modeling","Natural Language Processing Techniques","paper_W2167102709","Understanding the Demographics of Twitter Users","{\"openalex\":\"W2167102709\",\"doi\":\"10.1609/icwsm.v5i1.14168\"}","2026-05-15T00:26:01.199647964","2026-05-21T00:11:14.186453728","10.1609/icwsm.v5i1.14168","W2167102709","Every second, the thoughts and feelings of millions of people across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do not have an understanding of the Twitter population itself: Who are the Twitter users? How representative of the overall population are they? In this paper, we take the first steps towards answering these questions by analyzing data on a set of Twitter users representing over 1% of the U.S. population. We develop techniques that allow us to compare the Twitter population to the U.S. population along three axes (geography, gender, and race/ethnicity), and find that the Twitter population is a highly non-uniform sample of the population.","2021-08-03",2021,"Proceedings of the International AAAI Conference on Web and Social Media",780,[257,258,259],"https://ojs.aaai.org/index.php/ICWSM/article/view/14168","Complex Network Analysis Techniques","Advanced Text Analysis Techniques","Opinion Dynamics and Social Influence","paper_W4211017739","Machine behaviour","{\"openalex\":\"W4211017739\",\"doi\":\"10.1038/s41586-019-1138-y\"}","2026-05-14T12:04:25.216049503","2026-05-21T15:21:05.198056361","10.1038/s41586-019-1138-y","W4211017739","2019-04-01",2019,"Nature",1096,[273,183,274],"https://kops.uni-konstanz.de/server/api/core/bitstreams/382b3d2c-4715-4de8-875f-f6567aa78538/content","Ethics and Social Impacts of AI","Psychology of Moral and Emotional Judgment","paper_W2115022330","Measurement and analysis of online social networks","{\"openalex\":\"W2115022330\",\"doi\":\"10.1145/1298306.1298311\"}","2026-05-14T02:33:20.191015308","2026-05-21T04:30:03.18868751","10.1145/1298306.1298311","W2115022330","Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks.","2007-10-24",2007,3073,[257,259,167],{"_60":288,"_62":289},[],[290],{"_65":291,"_67":292,"_69":293,"_71":294},1784326643425,{"_74":296,"_76":77,"_78":291,"_80":-5,"_81":26,"_82":26,"_83":26,"_84":-5,"_85":-5,"_86":87,"_35":88,"_89":90},[73,295],"[\"portal\",\"industries\"]","industries",[297,298,299,300,301,302,303,304,305,306,307,308],{"_15":370,"_29":371,"_31":-5,"_32":372,"_33":373,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":374,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":371,"_59":373},{"_15":366,"_29":367,"_31":-5,"_32":-5,"_33":368,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":369,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":367,"_59":368},{"_15":360,"_29":361,"_31":-5,"_32":362,"_33":363,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":364,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":361,"_59":363},{"_15":356,"_29":357,"_31":-5,"_32":-5,"_33":358,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":359,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":357,"_59":358},{"_15":352,"_29":353,"_31":-5,"_32":-5,"_33":354,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":355,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":353,"_59":354},{"_15":346,"_29":347,"_31":-5,"_32":348,"_33":349,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":350,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":347,"_59":349},{"_15":341,"_29":342,"_31":-5,"_32":-5,"_33":343,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":344,"_321":26,"_322":345,"_323":26,"_324":-5,"_58":342,"_59":343},{"_15":337,"_29":338,"_31":-5,"_32":-5,"_33":339,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":340,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":338,"_59":339},{"_15":333,"_29":334,"_31":-5,"_32":-5,"_33":335,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":336,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":334,"_59":335},{"_15":329,"_29":330,"_31":-5,"_32":-5,"_33":331,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":332,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":330,"_59":331},{"_15":325,"_29":326,"_31":-5,"_32":-5,"_33":327,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":328,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":326,"_59":327},{"_15":309,"_29":310,"_31":-5,"_32":-5,"_33":311,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":312,"_42":-5,"_44":-5,"_313":314,"_315":-5,"_316":77,"_317":318,"_319":320,"_321":26,"_322":87,"_323":26,"_324":-5,"_58":310,"_59":311},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","children","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算",true,"tax_ind_blockchain","区块链",[351],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[365],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[375,376],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_379":24},"SITE_BASE_URL"]

Alan Mislove 人物

Alan Mislove · 相关文章