Internet Argument Corpus 2.0: An SQL schema for dialogic social media and the corpora to go with it

Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016

Volumn , Issue , 2016, Pages 4445-4452

  Abbott, Rob ^a   Ecker, Brian ^a   Anand, Pranav ^a   Walker, Marilyn A ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Argument mining; Data integration; Debate; Dialogue; Online forums; Sentiment; Stance

Indexed keywords

DATA INTEGRATION; NATURAL LANGUAGE PROCESSING SYSTEMS; QUERY PROCESSING; SOCIAL NETWORKING (ONLINE);

DEBATE; DIALOGUE; ONLINE FORUMS; SENTIMENT; STANCE;

LINGUISTICS;

EID: 85035751159     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (40)

1. Abbott, R., Walker, M., Jean E. Fox Tree, Anand, P., Bowmani, R., and King, J. (2011). How can you say such things?!?: Recognizing Disagreement in Informal Political Argument. In Proc. of the ACL Workshop on Language and Social Media.
2. Abu-Jbara, A., Diab, M., Dasigi, P., and Radev, D. (2012). Subgroup detection in ideological discussions. In Proc. of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1, ACL'12, pp. 399-409
3. Anand, P., Walker, M., Abbott, R., Jean E. Fox Tree, Bowmani, R., and Minor, M. (2011). Cats Rule and Dogs Drool: Classifying Stance in Online Debate. In Proc. of the ACL Workshop on Sentiment and Subjectivity.
4. Bamman, D. and Smith, N. A. (2015). Contextualized sarcasm detection on twitter. In Ninth Int. AAAI Conf. on Web and Social Media.
5. Biran, O. and Rambow, O. (2011). Identifying justifications in written dialogs. In 2011 Fifth IEEE Int. Conf. on Semantic Computing (ICSC), pp. 162-168. IEEE. ?
6. Boltuzic, F. and Snajder, J. (2014). Back up your stance: Recognizing arguments in online discussions. In Proc. of the First Workshop on Argumentation Mining, pp. 49-58.
7. Conrad, A., Wiebe, J., et al. (2012). Recognizing arguing subjectivity and argument tags. In Proc. of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, pp. 80-88. Association for Computational Linguistics.
8. Cook, J., Kenthapadi, K., and Mishra, N. (2013). Group chats on twitter. In Proc. of the 22nd international conference on World Wide Web, pp. 225-236. Int. World Wide Web Conf. s Steering Committee.
9. Cook, J., Das, A., Kenthapadi, K., and Mishra, N. (2014). Ranking twitter discussion groups. In Proc. of the second edition of the ACM conference on Online social networks, pp. 177-190. ACM.
10. Habernal, I. and Gurevych, I. (2015). Exploiting debate portals for semi-supervised argumentation mining in user-generated web discourse. In Proc. of the 2015 Conf. on Empirical Methods in Natural Language Processing, pp. 2127-2137.
11. Habernal, I. and Gurevych, I. (2016). Argumentation mining in user-generated web discourse. CoRR, abs/1601.02403.
12. Hasan, K. S. and Ng, V. (2013). Frame semantics for stance classification. In CoNLL, pp. 124-132.
13. Hasan, K. S. and Ng, V. (2014). Why are you taking this stance? identifying and classifying reasons in ideological debates. In Proc. of the Conf. on Empirical Methods in Natural Language Processing.
14. Hassan, A., Qazvinian, V., and Radev, D. (2010). What's with the attitude?: identifying sentences with attitude in online discussions. In Proc. of the 2010 Conf. on Empirical Methods in Natural Language Processing, pp. 1245-1255.
15. Hassan, A., Abu-Jbara, A., and Radev, D. (2012). Detecting subgroups in online discussions by modeling positive and negative relations among participants. In Proc. of the 2012 Joint Conf. on Empirical Methods in Natural Language Processing.
16. Jaech, A., Zayats, V., Fang, H., Ostendorf, M., and Hajishirzi, H. (2015). Talking to the crowd: What do people react to in online discussions? arXiv preprint arXiv:1507.02205.
17. Joshi, A., Sharma, V., and Bhattacharyya, P. (2015). Harnessing context incongruity for sarcasm detection. In Proc. of the 53rd Annual Meeting of the Association for Computational Linguistics, v 2, pp. 757-762.
18. Justo, R., Corcoran, T., Lukin, S. M., Walker, M., and Torres, M. I. (2014). Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web. Knowledge-Based Systems.
19. Lukin, S. and Walker, M. (2013). Really? well. apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue. NAACL 2013, page 30.
20. Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., and McClosky, D. (2014). The stanford corenlp natural language processing toolkit. In ACL (System Demonstrations), pp. 55-60.
21. Misra, A. and Walker, M. A. (2015). Topic independent identification of agreement and disagreement in social media dialogue. In Proc. of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue.
22. Misra, A., Anand, P., Jean E. Fox Tree, and Walker, M. (2015). Using summarization to discover argument facets in dialog. In Proc. of the 2015 NAACL Conf..
23. Murakami, A. and Raymond, R. (2010). Support or Oppose? Classifying Positions in Online Debates from Reply Activities and Opinion Expressions. In Proc. of the 23rd Int. Conf. on Comp. Ling., pp. 869-875.
24. Oraby, S., Reed, L., Compton, R., Riloff, E., Walker, M., and Whittaker, S. (2015). And thats a fact: Distinguishing factual and emotional argumentation in online dialogue. NAACL HLT 2015, page 116.
25. Pavlick, E. and Tetreault, J. (2016). An empirical analysis of formality in online communication. Transactions of the ACL, 4:61-74.
26. Prasad, R., Dinesh, N., Lee, A., Miltsakaki, E., Robaldo, L., Joshi, A., and Webber, B. (2008). The penn discourse treebank 2.0. In Proc. of the 6th Int. Conf. on Language Resources and Evaluation (LREC 2008), pp. 2961-2968.
27. Rosenthal, S. and McKeown, K. (2015). I couldnt agree more: The role of conversational structure in agreement and disagreement detection in online discussions. In 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 168.
28. Schlöder, J. J. and Fernández, R. (2014). The role of polarity in inferring acceptance and rejection in dialogue. In 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 151.
29. Somasundaran, S. and Wiebe, J. (2009). Recognizing stances in online debates. In Proc. of the 47th Annual Meeting of the ACL, pp. 226-234.
30. Somasundaran, S. and Wiebe, J. (2010). Recognizing stances in ideological on-line debates. In Proc. of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116-124.
31. Sridhar, D., Getoor, L., and Walker, M. (2014). Collective stance classification of posts in online debate forums. ACL 2014, page 109.
32. Sridhar, D., Foulds, J., Huang, B., Getoor, L., and Walker, M. (2015). Joint models of disagreement and stance in online debate. In Annual Meeting of the Association for Computational Linguistics (ACL).
33. Swanson, R., Lukin, S., Eisenberg, L., Corcoran, T. C., and Walker, M. A. (2014). Getting reliable annotations for sarcasm in online dialogues. In Language Resources and Evaluation Conf., LREC 2014.
34. Swanson, R., Ecker, B., and Walker, M. (2015). Argument mining: Extracting arguments from online dialogue. In Proc. of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 217-226. ACL.
35. Thomas, M., Pang, B., and Lee, L. (2006). Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In Proc. of the 2006 conference on empirical methods in NLP, EMNLP. pp. 327-335. ACL.
36. Walker, M., Anand, P., Abbott, R., Jean E. Fox Tree, Martell, C., and King, J. (2012a). That's your evidence?: Classifying stance in online political debate. Decision Support Sciences.
37. Walker, M., Anand, P., Abbott, R., and Fox Tree, J. E. (2012b). A corpus for research on deliberation and debate. In Language Resources and Evaluation Conf., LREC2012.
38. Walker, M., Anand, P., Abbott, R., and Grant, R. (2012c). Stance classification using dialogic properties of persuasion. In Meeting of the North American Association for Computational Linguistics. NAACL-HLT12.
39. Walker, M. A., Anand, P., Abbott, R., Tree, J. E. F., Martell, C., and King, J. (2012d). That is your evidence?: Classifying stance in online political debate. Decision Support Systems, 53(4):719-729.
40. Yin, J., Thomas, P., Narang, N., and Paris, C. (2012). Unifying local and global agreement and disagreement classification in online debates. In Proc. of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, WASSA'12, pp. 61-69