BibSLEIGH — James

Travelled to:

1 × Australia
1 × Chile
1 × China
1 × France
1 × Switzerland
1 × Taiwan
1 × United Kingdom
2 × Austria
2 × Canada
5 × USA

Collaborated with:

K.Y.Kamath Z.Cheng K.Lee S.Webb C.Cao J.McGee E.Khabiri L.Liu Y.Liang H.Lu D.Z.Sui C.Hsu S.Kashoob C.Pu J.Bae A.Fayazi A.C.Squicciarini H.Barthwal V.Bachani B.Eoff

Talks about:

social (10) base (8) media (7) web (7) communiti (4) approach (4) predict (4) locat (4) time (4) real (4)

Person: James Caverlee

DBLP: Caverlee:James

Contributed to:

2015

2014

2013

2012

2011

2010

2008

2006

2008

Wrote 24 papers:

ECIR-2015-CaoC #analysis #behaviour #detection #social #social media: Detecting Spam URLs in Social Media via Behavioral Analysis (CC, JC), pp. 703–714.
ECIR-2015-LiangCC #approach #detection #social #social media: A Noise-Filtering Approach for Spatio-temporal Event Detection in Social Media (YL, JC, CC), pp. 233–244.
RecSys-2015-LuC #personalisation #recommendation: Exploiting Geo-Spatial Preference for Personalized Expert Recommendation (HL, JC), pp. 67–74.
SIGIR-2015-FayaziLCS #crowdsourcing #online: Uncovering Crowdsourced Manipulation of Online Reviews (AF, KL, JC, ACS), pp. 233–242.
SIGIR-2014-ChengCBB #approach #twitter: Who is the barbecue king of texas?: a geo-spatial approach to finding local experts on twitter (ZC, JC, HB, VB), pp. 335–344.
CIKM-2013-KamathC #learning #predict #what: Spatio-temporal meme prediction: learning what hashtags will be popular where (KYK, JC), pp. 1341–1350.
CIKM-2013-McGeeCC #predict #social #social media: Location prediction in social media based on tie strength (JM, JC, ZC), pp. 459–468.
HT-2013-LiangCCK #how #modelling #social #social media: How big is the crowd?: event and location based population modeling in social media (YL, JC, ZC, KYK), pp. 99–108.
CIKM-2012-KamathC #realtime #retrieval #web: Content-based crowd retrieval on the real-time web (KYK, JC), pp. 195–204.
CIKM-2012-KamathCCS #community #modelling #social #social media: Spatial influence vs. community influence: modeling the global spread of social media (KYK, JC, ZC, DZS), pp. 962–971.
HT-2012-KhabiriCK #predict #realtime #semantics #web: Predicting semantic annotations on the real-time web (EK, JC, KYK), pp. 219–228.
CIKM-2011-ChengCKL #towards #web: Toward traffic-driven location-based web search (ZC, JC, KYK, KL), pp. 805–814.
CIKM-2011-KamathC: Discovering trending phrases on information streams (KYK, JC), pp. 2245–2248.
CIKM-2011-LeeCCS #detection #social #social media: Content-driven detection of campaigns in social media (KL, JC, ZC, DZS), pp. 551–556.
CIKM-2011-McGeeCC #social #social media: A geographic study of tie strength in social media (JM, JC, ZC), pp. 2333–2336.
SAC-2011-HsuCK #clustering: Hierarchical comments-based clustering (CFH, JC, EK), pp. 1130–1137.
SIGIR-2011-CaverleeCEHKM #monitoring #named #realtime #web: CrowdTracker: enabling community-based real-time web monitoring (JC, ZC, BE, CFH, KYK, JM), pp. 1283–1284.
CIKM-2010-ChengCL #approach #twitter: You are where you tweet: a content-based approach to geo-locating twitter users (ZC, JC, KL), pp. 759–768.
CIKM-2010-KamathC #identification #realtime #web: Identifying hotspots on the real-time web (KYK, JC), pp. 1837–1840.
HT-2010-KashoobCK #ranking #social #web: Community-based ranking of the social web (SK, JC, KYK), pp. 141–150.
SIGIR-2010-LeeCW #machine learning #social: Uncovering social spammers: social honeypots + machine learning (KL, JC, SW), pp. 435–442.
CIKM-2008-WebbCP #predict #web: Predicting web spam with HTTP session information (SW, JC, CP), pp. 339–348.
SIGIR-2006-CaverleeLB #approach #distributed #query: Distributed query sampling: a quality-conscious approach (JC, LL, JB), pp. 340–347.
JCDL-2008-CaverleeLW #community #named #online #trust: Socialtrust: tamper-resilient trust establishment in online communities (JC, LL, SW), pp. 104–114.