ToWikify

}

Paper	Related Paper	Student Andrew ID
Measuring_User_Influence_in_Twitter:_The_Million_Follower_Fallacy	Influentials, Networks, and Public Opinion Formation [1]
Vladimir_Ouzienko,_Prediction_of_Attributes_and_Links_in_Temporal_Social_Networks	Introduction to stochastic actor-based models for network dynamics [2]
OConnor_et._al.,_ICWSM_2010	Widespread Worry and the Stock Market [3]
Y._Borghol_et_al._Performance_Evaluation_68_2011	The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity [4]
Rodriguez_et_al_Oct_2011	The origin of bursts and heavy tails in human dynamics [5]
Zheleva_and_Getoor,_WWW2009	Preserving the privacy of sensitive relationships in graph data. PinKDD, 2007 [6]
Ramage_et_al_ICWSM_2010	Is it Really About Me? Message Content in Social Awareness Streams [Ramage_et_al_ICWSM_2010]
Reviewing_social_media_use_by_clinicians	Integrating the hospital library with patient care, teaching and research: model and Web 2.0 tools to create a social and collaborative community of clinical research in a hospital setting. [7]
Yeh_et_al_WikiWalk_Random_walks_on_Wikipedia_for_Semantic_Relatedness	Personalizing PageRank for Word Sense Disambiguation [8]
Mrinmaya_et._al._WWW'12	The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email [9]
Yano_et_al_ICWSM_2010._What’s_Worthy_of_Comment?_Content_and_Comment_Volume_in_Political_Blogs	Mixed membership models of scientific publication [10]
Yang_et_al_Modeling_Information_Diffusion_in_Implicit_Networks	Inferring the Diffusion and Evolution of Topics in Social Communities [11]
Link_propagation:_A_fast_semi-supervised_learning_algorithm_for_link_prediction	Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs [12]
Modeling_Contagion_Through_Facebook_News_Feed	Cascading Behavior in Large Blog Graphs [cs.stanford.edu/~jure/pubs/blogs-sdm07.pdf]
Yano_et_al_NAACL_2009	Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs [13]
Rosen-Zvi_et_al,_The_Author-Topic_Model_for_Authors_and_Documents	The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity [14]
Ritter_et_al_NAACL_2010._Unsupervised_Modeling_of_Twitter_Conversations	Catching the drift: Probabilistic content models, with applications to generation and summarization [15]
Zheleva_ACM_2009	Geographic routing in social networks [16]
Ritter_et_al,_EMNLP_2011._Named_Entity_Recognition_in_Tweets:_An_Experimental_Study	Event discovery in social media feeds [17]
Miller_et_al_ICWSM_2011	Can predicate-argument structures be used for contextual opinion retrieval from blogs? [18]
Capturing_Global_Mood_Levels_using_Blog_Posts	Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena [19]
Accurate_Unlexicalized_Parsing	Learning Accurate, Compact, and Interpretable Tree Annotation, S. Petrov, L. Barrett, R. Thibaux, D. Klein, ACL 2006 [20]
Hassan_et_al,_ICWSM_2009	Document representation and query expansion models for blog recommendation [21]
Akcora_et_al,_SOMA_2010	L. Ku, Y. Liang, and H. Chen. Opinion extraction, summarization and tracking in news and blog corpora. In Proceedings of AAAI-2006 [22]
Collier_et_al._Journal_of_Biomedical_Semantics_2011	Modeling Spread of Disease from Social Interaction [23]
Anderson_et_al_KDD2012	Predicting web searcher satisfaction with existing community-based answers [24]
Davidov_et_al_COLING_10	Structured Models for Fine-to-Coarse Sentiment Analysis [25]
BinLu_et_al._ACL2011	Learning Multilingual Subjective Language via Cross-Lingual Projections [26]
A_Latent_Variable_Model_for_Geographic_Lexical_Variation	Q. Mei, C. Liu, H. Su, and C. X Zhai. 2006. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In Proceedings of WWW [27]
Chambers_and_Jurafsky,_Unsupervised_Learning_of_Narrative_Event_Chains,_ACL_2008	Chklovski and Pantel (2004) Verbocean:Mining the web for fine-grained semantic verb relations [28]
Leskovec_et_al.,_WWW_2010	Statistical properties of community structure in large social and information networks. In WWW ’08 [29]
Esuli_and_Sebastiani_LREC_2006	Determining term subjectivity and term orientation for opinion mining. [30]
Gilbert_et_al.,_ICWSM_2010	A Sentiment Detection Engine for Internet Stock Message Boards [31]
Domain-Assisted_Product_Aspect_Hierarchy_Generation:_Towards_Hierarchical_Organization_of_Unstructured_Consumer_Reviews	Learning object models from semistructured Web documents [32]
Andreevskaia_et_al.,_ICWSM_2007	M. Hurst and K. Nigam. Retrieving topical sentiments from online document collection. [33]
E.A._Leicht,_Structure_of_Time_Evo_citation_networks_2007	Detecting Topic Evolution in Scientific Literature: How Can Citations Help? [34]
A_Discriminative_Latent_Variable_Model_for_SMT	An End-to-End Discriminative Approach to Machine Translation [35]
Agarwal_et_al,_ICWSM_2009#Related_Works_and_Papers	Latent Friend Mining from Blog Data, ICDM 2006 [36]
Birke&Sarkar,FigLanguages07	A Clustering Approach for the Nearly Unsupervised Recognition of Nonliteral Language, EACL-2006 [37]
Das_Sarma_et._al.,_Dynamic_Relationship_and_Event_Discovery,_WSDM_2011	Q. Zhao, P. Mitra, and B. Chen. Temporal and information flow based event detection from social text streams. In AAAI, 2007 [38]