Search For:

Displaying 1-41 out of 41 total
Identity, Privacy, and Deception in Social Networks
Found in: IEEE Internet Computing
By Elisa Bertino,James Caverlee,Elena Ferrari
Issue Date:March 2014
pp. 7-9
This special issue focuses on new risks and growing concerns centered around identity, privacy, and deception in the context of Internet-enabled social networks. The four articles in this issue address a range of issues in social networks and can serve as ...
   
A Summary of Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities
Found in: Granular Computing, IEEE International Conference on
By Steve Webb, James Caverlee, Calton Pu
Issue Date:August 2010
pp. 39-40
Online social networking communities are connecting hundreds of millions of individuals across the globe and facilitating new modes of interaction. Due to their immense popularity, an important question is whether these communities are safe for their users...
 
Probabilistic Generative Models of the Social Annotation Process
Found in: Computational Science and Engineering, IEEE International Conference on
By Said Kashoob, James Caverlee, Elham Khabiri
Issue Date:August 2009
pp. 42-49
With the growth in the past few years of social tagging services like Delicious and CiteULike, there is growing interest in modeling and mining these social systems for deriving implicit social collective intelligence. In this paper, we propose and explore...
 
Ranking Comments on the Social Web
Found in: Computational Science and Engineering, IEEE International Conference on
By Chiao-Fang Hsu, Elham Khabiri, James Caverlee
Issue Date:August 2009
pp. 90-97
We study how an online community perceives the relative quality of its own user-contributed content, which has important implications for the successful self-regulation and growth of the Social Web in the presence of increasing spam and a flood of Social W...
 
Exploring Feedback Models in Interactive Tagging
Found in: Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on
By Robert Graham, James Caverlee
Issue Date:December 2008
pp. 141-147
One of the cornerstones of the Social Web is informal user-generated metadata (or tags) for annotating web objects like pages, images, and videos. However, many real-world domains are currently left out of the social tagging phenomenon due to the lack of a...
 
A Parameterized Approach to Spam-Resilient Link Analysis of the Web
Found in: IEEE Transactions on Parallel and Distributed Systems
By James Caverlee, Steve Webb, Ling Liu, William B. Rouse
Issue Date:October 2009
pp. 1422-1438
Link-based analysis of the Web provides the basis for many important applications—like Web search, Web-based data mining, and Web page categorization—that bring order to the massive amount of distributed Web content. Due to the overwhelming reliance on the...
 
DSphere: A Source-Centric Approach to Crawling, Indexing and Searching the World Wide Web
Found in: Data Engineering, International Conference on
By Bhuvan Bamba, Ling Liu, James Caverlee, Vaibhav Padliya, Mudhakar Srivatsa, Tushar Bansal, Mahesh Palekar, Joseph Patrao, Suiyang Li, Aameek Singh
Issue Date:April 2007
pp. 1515-1516
We describe DSPHERE a decentralized system for crawling, indexing, searching and ranking of documents in the World Wide Web. Unlike most of the existing search technologies that depend heavily on a page-centric view of the Web, we advocate a source-centric...
 
Spam-Resilient Web Rankings via Influence Throttling
Found in: Parallel and Distributed Processing Symposium, International
By James Caverlee, Steve Webb, Ling Liu
Issue Date:March 2007
pp. 43
Web search is one of the most critical applications for managing the massive amount of distributed Web content. Due to the overwhelming reliance on Web search, there is a rise in efforts to manipulate (or spam) Web search engines. In this paper, we develop...
 
Process Mining, Discovery, and Integration using Distance Measures
Found in: Web Services, IEEE International Conference on
By Joonsoo Bae, Ling Liu, James Caverlee, William B. Rouse
Issue Date:September 2006
pp. 479-488
Business processes continue to play an important role in today?s service-oriented enterprise computing systems. Mining, discovering, and integrating processoriented services has attracted growing attention in the recent year. In this paper we present a qua...
 
QA-Pagelet: Data Preparation Techniques for Large-Scale Data Analysis of the Deep Web
Found in: IEEE Transactions on Knowledge and Data Engineering
By James Caverlee, Ling Liu
Issue Date:September 2005
pp. 1247-1262
This paper presents the QA-Pagelet as a fundamental data preparation technique for large-scale data analysis of the Deep Web. To support QA-Pagelet extraction, we present the Thor framework for sampling, locating, and partioning the QA-Pagelets from the De...
 
Domain-Specific Web Service Discovery with Service Class Descriptions
Found in: Web Services, IEEE International Conference on
By Daniel Rocco, James Caverlee, Ling Liu, Terence Critchlow
Issue Date:July 2005
pp. 481-488
This paper presents DynaBot, a domain-specific web service discovery system. The core idea of the DynaBot service discovery system is to use domain-specific service class descriptions powered by an intelligent Deep Web crawler. In contrast to current regis...
 
Probe, Cluster, and Discover: Focused Extraction of QA-Pagelets from the Deep Web
Found in: Data Engineering, International Conference on
By James Caverlee, Ling Liu, David Buttler
Issue Date:April 2004
pp. 103
In this paper, we introduce the concept of a QA-Pagelet to refer to the content region in a dynamic page that contains query matches. We present THOR, a scalable and efficient mining system for discovering and extracting QA-Pagelets from the Deep Web. A un...
 
Campaign extraction from social media
Found in: ACM Transactions on Intelligent Systems and Technology (TIST)
By Daniel Z. Sui, James Caverlee, Kyumin Lee, Zhiyuan Cheng
Issue Date:December 2013
pp. 1-28
In this manuscript, we study the problem of detecting coordinated free text campaigns in large-scale social media. These campaigns—ranging from coordinated spam messages to promotional and advertising campaigns to political astro-turfing—are gr...
     
DUBMOD13: international workshop on data-driven user behavioral modelling and mining from social media
Found in: Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (CIKM '13)
By Jalal Mahmud, James Caverlee, Jeffrey Nichols, John O'Donovan, Michelle X. Zhou
Issue Date:October 2013
pp. 2551-2552
Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. These data can be used to better understand people (e.g., personality traits, perceptions, and preferences) and predict their behavior. As a result, a deeper u...
     
Spatio-temporal meme prediction: learning what hashtags will be popular where
Found in: Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (CIKM '13)
By James Caverlee, Krishna Y. Kamath
Issue Date:October 2013
pp. 1341-1350
In this paper, we tackle the problem of predicting what online memes will be popular in what locations. Specifically, we develop data-driven approaches building on the global footprint of 755 million geo-tagged hashtags spread via Twitter. Our proposed met...
     
Location prediction in social media based on tie strength
Found in: Proceedings of the 22nd ACM international conference on Conference on information & knowledge management (CIKM '13)
By Zhiyuan Cheng, James Caverlee, Jeffrey McGee
Issue Date:October 2013
pp. 459-468
We propose a novel network-based approach for location estimation in social media that integrates evidence of the social tie strength between users for improved location estimation. Concretely, we propose a location estimator -- FriendlyLocation -- that le...
     
How big is the crowd?: event and location based population modeling in social media
Found in: Proceedings of the 24th ACM Conference on Hypertext and Social Media (HT '13)
By James Caverlee, Krishna Y. Kamath, Yuan Liang, Zhiyuan Cheng
Issue Date:May 2013
pp. 99-108
In this paper, we address the challenge of modeling the size, duration, and temporal dynamics of short-lived crowds that manifest in social media. Successful population modeling for crowds is critical for many services including location recommendation, tr...
     
A content-driven framework for geolocating microblog users
Found in: ACM Transactions on Intelligent Systems and Technology (TIST)
By James Caverlee, Kyumin Lee, Zhiyuan Cheng
Issue Date:January 2013
pp. 1-27
Highly dynamic real-time microblog systems have already published petabytes of real-time human sensor data in the form of status updates. However, the lack of user adoption of geo-based features per user or per post signals that the promise of microblog se...
     
Public checkins versus private queries: measuring and evaluating spatial preference
Found in: Proceedings of the 5th International Workshop on Location-Based Social Networks (LBSN '12)
By James Caverlee, Roger Liew, Wai Gen Yee, Yuan Liang, Zhiyuan Cheng
Issue Date:November 2012
pp. 40-47
Understanding the spatial preference of mobile and web users is of great significance to creating and improving location-based recommendation systems, travel planners, search engines, and other emerging mobile applications. However, traditional sources of ...
     
DUBMMSM'12: international workshop on data-driven user behavioral modeling and mining from social media
Found in: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12)
By Jalal Mahmud, James Caverlee, Jeffrey Nichols, John O' Donovan, Michelle Zhou
Issue Date:October 2012
pp. 2752-2753
Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. This data can be used to better understand people, such as their personality traits, perceptions, and preferences, and predict their behavior. This deeper unde...
     
Spatial influence vs. community influence: modeling the global spread of social media
Found in: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12)
By Daniel Z. Sui, James Caverlee, Krishna Y. Kamath, Zhiyuan Cheng
Issue Date:October 2012
pp. 962-971
In this paper we seek to understand and model the global spread of social media. How does social media spread from location to location across the globe? Can we model this spread and predict where social media will be popular in the future? Toward answerin...
     
Content-based crowd retrieval on the real-time web
Found in: Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12)
By James Caverlee, Krishna Y. Kamath
Issue Date:October 2012
pp. 195-204
In this paper, we propose and evaluate a novel content-driven crowd discovery algorithm that can efficiently identify newly-formed communities of users from the real-time web. Short-lived crowds reflect the real-time interests of their constituents and pro...
     
Predicting semantic annotations on the real-time web
Found in: Proceedings of the 23rd ACM conference on Hypertext and social media (HT '12)
By Elham Khabiri, James Caverlee, Krishna Y. Kamath
Issue Date:June 2012
pp. 219-228
The explosion of the real-time web has spurred a growing need for new methods to organize, monitor, and distill relevant information from these large-scale social streams. One especially encouraging development is the self-curation of the real-time web via...
     
Detecting collective attention spam
Found in: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality (WebQuality '12)
By James Caverlee, Krishna Y. Kamath, Kyumin Lee, Zhiyuan Cheng
Issue Date:April 2012
pp. 48-55
We examine the problem of collective attention spam, in which spammers target social media where user attention quickly coalesces and then collectively focuses around a phenomenon. Compared to many existing spam types, collective attention spam relies on t...
     
A geographic study of tie strength in social media
Found in: Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11)
By James A. Caverlee, Jeffrey McGee, Zhiyuan Cheng
Issue Date:October 2011
pp. 2333-2336
In this paper, we investigate the interplay of distance and tie strength through an examination of 20 million geo-encoded tweets collected from Twitter and 6 million user profiles. Concretely, we investigate the relationship between the strength of the tie...
     
Discovering trending phrases on information streams
Found in: Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11)
By James Caverlee, Krishna Y. Kamath
Issue Date:October 2011
pp. 2245-2248
We study the problem of efficient discovery of trending phrases from high-volume text streams -- be they sequences of Twitter messages, email messages, news articles, or other time-stamped text documents. Most existing approaches return top-k trending phra...
     
Toward traffic-driven location-based web search
Found in: Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11)
By James Caverlee, Krishna Yeswanth Kamath, Kyumin Lee, Zhiyuan Cheng
Issue Date:October 2011
pp. 805-814
The emergence of location sharing services is rapidly accelerating the convergence of our online and offline activities. In one direction, Foursquare, Google Latitude, Facebook Places, and related services are enriching real-world venues with the social an...
     
Content-driven detection of campaigns in social media
Found in: Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11)
By Daniel Z. Sui, James Caverlee, Kyumin Lee, Zhiyuan Cheng
Issue Date:October 2011
pp. 551-556
We study the problem of detecting coordinated free text campaigns in large-scale social media. These campaigns -- ranging from coordinated spam messages to promotional and advertising campaigns to political astro-turfing -- are growing in significance and ...
     
CrowdTracker: enabling community-based real-time web monitoring
Found in: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information (SIGIR '11)
By Brian Eoff, Chiao-Fang Hsu, James Caverlee, Jeffrey McGee, Krishna Kamath, Zhiyuan Cheng
Issue Date:July 2011
pp. 1283-1284
CrowdTracker is a community-based web monitoring system optimized for real-time web streams like Twitter, Facebook, and Google Buzz. In this demo summary, we provide an overview of the system and architecture, and outline the demonstration plan.
     
Hierarchical comments-based clustering
Found in: Proceedings of the 2011 ACM Symposium on Applied Computing (SAC '11)
By Chiao-Fang Hsu, Elham Khabiri, James Caverlee
Issue Date:March 2011
pp. 1130-1137
Information resources on the Web like videos, images, and documents are increasingly becoming more "social" through user engagement via commenting systems. These commenting systems provide a forum for users to discuss the resources but have the side effect...
     
Identifying hotspots on the real-time web
Found in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10)
By James Caverlee, Krishna Yeswanth Kamath
Issue Date:October 2010
pp. 1837-1840
We study the problem of automatically identifying ``hotspots'' on the real-time web. Concretely, we propose to identify highly-dynamic ad-hoc collections of users -- what we refer to as crowds -- in massive social messaging systems like Twitter and Faceboo...
     
You are where you tweet: a content-based approach to geo-locating twitter users
Found in: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM '10)
By James Caverlee, Kyumin Lee, Zhiyuan Cheng
Issue Date:October 2010
pp. 759-768
We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues. By augmenting the massive human-powered sensing capabi...
     
Uncovering social spammers: social honeypots + machine learning
Found in: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '10)
By James Caverlee, Kyumin Lee, Steve Webb
Issue Date:July 2010
pp. 435-442
Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminator...
     
Predicting web spam with HTTP session information
Found in: Proceeding of the 17th ACM conference on Information and knowledge mining (CIKM '08)
By Calton Pu, James Caverlee, Steve Webb
Issue Date:October 2008
pp. 1001-1001
Web spam is a widely-recognized threat to the quality and security of the Web. Web spam pages pollute search engine indexes, burden Web crawlers and Web mining services, and expose users to dangerous Web-borne malware. To defend against Web spam, most prev...
     
Socialtrust: tamper-resilient trust establishment in online communities
Found in: Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries (JCDL '08)
By James Caverlee, Ling Liu, Steve Webb
Issue Date:June 2008
pp. 597-617
Web 2.0 promises rich opportunities for information sharing, electronic commerce, and new modes of social interaction, all centered around the "social Web" of user-contributed content, social annotations, and person-to-person social connections. But the in...
     
Plurality: a context-aware personalized tagging system
Found in: Proceeding of the 17th international conference on World Wide Web (WWW '08)
By Brian Eoff, James Caverlee, Robert Graham
Issue Date:April 2008
pp. 1-7
We present the design of Plurality, an interactive tagging system. Plurality's modular architecture allows users to automatically generate high-quality tags over Web content, as well as over archival and personal content typically beyond the reach of exist...
     
Towards robust trust establishment in web-based social networks with socialtrust
Found in: Proceeding of the 17th international conference on World Wide Web (WWW '08)
By James Caverlee, Ling Liu, Steve Webb
Issue Date:April 2008
pp. 1-7
We propose the SocialTrust framework for tamper-resilient trust establishment in online social networks. Two of the salient features of SocialTrust are its dynamic revision of trust by (i) distinguishing relationship quality from trust; and (ii) incorporat...
     
Countering web spam with credibility-based link analysis
Found in: Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing (PODC '07)
By James Caverlee, Ling Liu
Issue Date:August 2007
pp. 157-166
We introduce the concept of link credibility, identify the conflation of page quality and link credibility in popular Web link analysis algorithms, and discuss how to decouple link credibility from page quality. Our credibility-based link analysis exhibits...
     
Distributed query sampling: a quality-conscious approach
Found in: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR '06)
By James Caverlee, Joonsoo Bae, Ling Liu
Issue Date:August 2006
pp. 340-347
We present an adaptive distributed query-sampling framework that is quality-conscious for extracting high-quality text database samples. The framework divides the query-based sampling process into an initial seed sampling phase and a quality-aware iterativ...
     
Exploiting the deep web with DynaBot: matching, probing, and ranking
Found in: Special interest tracks and posters of the 14th international conference on World Wide Web (WWW '05)
By Daniel Rocco, James Caverlee, Ling Liu, Terence Critchlow
Issue Date:May 2005
pp. 1174-1175
We present the design of Dynabot, a guided Deep Web discovery system. Dynabot's modular architecture supports focused crawling of the Deep Web with an emphasis on matching, probing, and ranking discovered sources using two key components: service class des...
     
Discovering and ranking web services with BASIL: a personalized approach with biased focus
Found in: Proceedings of the 2nd international conference on Service oriented computing (ICSOC '04)
By Daniel Rocco, James Caverlee, Ling Liu
Issue Date:November 2004
pp. 153-162
In this paper we present a personalized web service discovery and ranking technique for discovering and ranking relevant data-intensive web services. Our first prototype -- called BASIL -- supports a <i>personalized</i> view of data-intensive web s...
     
 1