Mining newsgroups using networks arising from social behavior

By Rakesh Agrawal, Sridhar Rajagopalan, Rayadurgam Srikant, Yirong Xu. The Web Conference, 2003

310 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/22291dcd8b5255b6d0d51a86138ee5bf57b34550

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bing Liu
2. B. Liu
3. Mohit Bansal
4. Erik Cambria
5. Yejin Choi
6. M. Zhou
7. Janyce Wiebe
8. Jianfeng Gao

In Statistical and Nonlinear Physics

1. Jie Tang
2. Chenhao Tan
3. Christopher Potts
4. Robert West
5. Ping Li
6. Hristo S. Paskov
7. Jure Leskovec
8. M. Zhou

Citations in the corpus

• A Corpus for Research on Deliberation and Debate (2012, background): “One thread approaches the problem from the perspective of social structures and social network models (Agrawal et al., 2003; Murakami and Raymond, 2010).”
• User-level sentiment analysis incorporating social networks (2011, background): “A number of different graphs have been exploited in documentor sentence-level sentiment analysis [2, 14, 1, 15, 12, 21, 26, 22, 8], including in a semi-supervised setting [5, 6, 18].”
• Recognizing Stances in Ideological On-Line Debates (2010, background): “…of features associated with the stances in Gay Rights domain Discourse-level participant relation, that is, whether participants agree/disagree has been found useful for determining political side-taking (Thomas et al., 2006; Bansal et al., 2008; Agrawal et al., 2003; Malouf and Mullen, 2008).”
• Learning Subjective Language (2004, background): “Agrawal et al. (2003) partition newsgroup authors into camps based on quotation links.”
• Exploiting Social Network Structure for Person-to-Person Sentiment Analysis (2014, background): “This involves elements of work on modeling political affiliation (Agrawal et al., 2003; Mal-ouf and Mullen, 2008; Yu et al., 2008), bias (Yano et al., 2010; Recasens et al., 2013), and stance on debate topics (Thomas et al., 2006; Somasundaran and Wiebe, 2010; Lin et al., 2006; Anand et al., 2011),…”
• Opinion Mining and Sentiment Analysis (2008, background): “typically done to indicate negative sentiment toward the cited post [4].”
• Opinion observer: analyzing and comparing opinions on the Web (2005, background): “[1] finds that supervised sentiment classification is inaccurate.”
• Mining email social networks (2006, background): “Quoted text content (as done in [1]) is one approach, whereby one message is identified as a reply if it quotes text from another.”
• Get out the vote: Determining support or opposition from Congressional floor-debate transcripts (2006, unknown): “inter-document references in the form of hyperlinks (Agrawal et al., 2003).”
• Stance Classification using Dialogic Properties of Persuasion (2012, background): “…2008; Yessenalina et al., 2010; Balahur et al., 2009; Bur-foot et al., 2011); (2) company-internal discussion sites (Murakami and Raymond, 2010; Agrawal et al., 2003); and (3) online social and political public forums (Somasundaran and Wiebe, 2009; Somasun-daran and Wiebe, 2010; Wang and…”
• Classifying Party Affiliation from Political Speech (2008, background): “In the context of political speech, a common goal, especially among computer scientists, has been the construction of general-purpose political opinion classifiers because of their potential applications in e-rulemaking and mass media analysis ( Agrawal, Rajagopalan, Srikant, & Xu, 2003 ; Kwon et a”