acl acl2013 acl2013-121 knowledge-graph by maker-knowledge-mining

121 acl-2013-Discovering User Interactions in Ideological Discussions


Source: pdf

Author: Arjun Mukherjee ; Bing Liu

Abstract: Online discussion forums are a popular platform for people to voice their opinions on any subject matter and to discuss or debate any issue of interest. In forums where users discuss social, political, or religious issues, there are often heated debates among users or participants. Existing research has studied mining of user stances or camps on certain issues, opposing perspectives, and contention points. In this paper, we focus on identifying the nature of interactions among user pairs. The central questions are: How does each pair of users interact with each other? Does the pair of users mostly agree or disagree? What is the lexicon that people often use to express agreement and disagreement? We present a topic model based approach to answer these questions. Since agreement and disagreement expressions are usually multiword phrases, we propose to employ a ranking method to identify highly relevant phrases prior to topic modeling. After modeling, we use the modeling results to classify the nature of interaction of each user pair. Our evaluation results using real-life discussion/debate posts demonstrate the effectiveness of the proposed techniques.

Reference: text


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 In forums where users discuss social, political, or religious issues, there are often heated debates among users or participants. [sent-2, score-0.384]

2 Existing research has studied mining of user stances or camps on certain issues, opposing perspectives, and contention points. [sent-3, score-0.356]

3 In this paper, we focus on identifying the nature of interactions among user pairs. [sent-4, score-0.257]

4 Since agreement and disagreement expressions are usually multiword phrases, we propose to employ a ranking method to identify highly relevant phrases prior to topic modeling. [sent-9, score-1.001]

5 After modeling, we use the modeling results to classify the nature of interaction of each user pair. [sent-10, score-0.3]

6 There have been some related works that focus on discovering the general topics and ideological perspectives in online discussions (Ahmed and Xing, 2010), placing users in support/oppose camps (Agarwal et al. [sent-22, score-0.645]

7 , 2003), and classifying user stances (Somasundaran and Wiebe, 2009). [sent-23, score-0.23]

8 However, these works are at a rather coarser level and have not considered more fine-grained characteristics of debates/discussions where users interact with each other by quoting/replying each other to express agreement or disagreement and argue with one another. [sent-24, score-0.567]

9 The nature of interaction of each pair of users or participants who have engaged in the discussion of certain issues, i. [sent-26, score-0.396]

10 What language expressions are often used to express agreement (e. [sent-30, score-0.254]

11 , “I agree” and “you’re right”) and disagreement (e. [sent-32, score-0.312]

12 We note that although agreement and disagreement expressions are distinct from traditional sentiment expressions (words and phrases) such as good, excellent, bad, and horrible, agreement and disagreement clearly express a kind of sentiment as well. [sent-35, score-1.438]

13 They are usually emitted during interactive exchanges of arguments in ideological discussions. [sent-36, score-0.272]

14 We define the polarity of agreement expressions as positive and the polarity of disagreement expressions as negative. [sent-38, score-0.642]

15 We refer agreement and disagreement expressions as ADsentiment expressions, or AD-expressions for short. [sent-39, score-0.566]

16 AD-expressions are crucial for the analysis of interactive discussions and debates just as sentiment expressions are instrumental in sentiment analysis (Liu, 2012). [sent-40, score-0.558]

17 In our earlier work (Mukherjee and Liu, 2012a), we proposed three topic models to mine contention points, which also extract ADexpressions. [sent-44, score-0.205]

18 In this paper, we further improve the work by coupling an information retrieval method to rank good candidate phrases with topic modeling in order to discover more accurate ADexpressions. [sent-45, score-0.385]

19 Furthermore, we apply the resulting AD-expressions to the new task of classifying the arguing or interaction nature of each pair of users. [sent-46, score-0.496]

20 We employ a semi-supervised generative model called JTE-P to jointly model AD-expressions, pair interactions, and discussion topics simultaneously in a single framework. [sent-48, score-0.3]

21 For example, we can discover the most contentious pairs for each topic and ideological camps of participants, i. [sent-50, score-0.38]

22 As discussed earlier, agreement and disagreement are a special form of sentiments and are different from the sentiment studied in the mainstream research. [sent-60, score-0.702]

23 Traditional sentiment is mainly expressed with sentiment terms (e. [sent-61, score-0.387]

24 , great and bad), while agreement and disagreement are inferred by AD-expressions (e. [sent-63, score-0.49]

25 Topic models: Our work is also related to topic modeling and joint modeling of topics and other information as we jointly model several aspects of discussions/debates. [sent-67, score-0.353]

26 Yet other approaches extend topic models to produce author specific topics (Rosen-Zvi et al. [sent-73, score-0.31]

27 However, these models do not model debates and hence are unable to discover AD-expressions and interaction natures of author pairs. [sent-76, score-0.301]

28 Also related are topic models in sentiment analysis which are often referred to as Aspect and Sentiment models (ASMs). [sent-77, score-0.298]

29 , discovering positive and negative topic words and sentiments for each topic without separating topic and sentiment terms) (e. [sent-80, score-0.701]

30 , 2003), speaker 672 utterances were classified into agreement, disagreement and backchannel classes. [sent-107, score-0.356]

31 , 2013), mining opposing perspectives (Lin and Hauptmann, 2006), linguistic accommodation (Mukherjee and Liu, 2012c), and contention point mining (Mukherjee and Liu, 2012a). [sent-115, score-0.285]

32 We propose a new method to improve the AD-expression mining and a new task of classifying pair interaction nature to determine whether each pair of users who have interacted based on replying relations mostly agree or disagree with each other. [sent-117, score-0.748]

33 JTE-P is a semi-supervised generative model motivated by the joint occurrence of expression types (agreement and disagreement), topics in discussion posts, and user pairwise interactions. [sent-119, score-0.391]

34 In a typical debate/discussion post, the user (author) mentions a few topics (using semantically related topical terms) and expresses some viewpoints with one or more ADexpression types (using agreement and disagreement expressions). [sent-121, score-0.856]

35 In our crawled dataset, 77% of all posts exhibit explicit quoting/reply-to relations excluding the first posts of threads which start the discussions and usually have nobody to quote/reply-to. [sent-126, score-0.394]

36 The discussion topics and AD-expressions emitted are thus caused by the author-pairs’ topical interests and their nature of interaction (agreeing vs. [sent-128, score-0.592]

37 com, we found that a pair of users typically exhibited a dominant arguing nature Figure 1: JTE-P Model in plate notation. [sent-131, score-0.465]

38 exhibits shared topics and arguing nature of various pairs, ? [sent-151, score-0.377]

39 More precisely, the pair specific topic and AD-expression … distributions ( ? [sent-159, score-0.229]

40 ) “shape” the topics and AD-expressions emitted in ? [sent-165, score-0.218]

41 as agreement and disagreement on topical viewpoints are directed towards certain target authors. [sent-166, score-0.674]

42 = 2 as in debates, we mostly find two expression types: agreement and disagreement (more details in §6. [sent-180, score-0.601]

43 Instead of using all n-grams, a relevance based ranking method is proposed to select a subset of highly relevant n-grams for model building (details in §4). [sent-186, score-0.311]

44 The idea is motivated by the observation that topical and AD-expression terms usually play different roles in a sentence. [sent-205, score-0.22]

45 674 4 Phrase Ranking based on Relevance We now detail our method of pre-processing ngrams (phrases) based on relevance to select a subset of highly relevant n-grams for model building. [sent-607, score-0.26]

46 For each word, a topic is sampled first, then its status as a unigram or bigram is sampled, and finally the word is sampled from a topic-specific unigram or bigram distribution. [sent-617, score-0.257]

47 Yet another thread of research post-processes the discovered topical unigrams to form multiword phrases using likelihood scores (Blei and Lafferty, 2009). [sent-620, score-0.465]

48 Next, we rank the candidate phrases (n-grams) using our probabilistic ranking function. [sent-652, score-0.228]

49 The ranking function is grounded on the following hypothesis: a relevant phrase is one whose unigrams are closely related to (or appear with high probabilities in) the given AD-expression type, ? [sent-653, score-0.248]

50 , 201 1) for deriving our relevance ranking function as follows: ? [sent-691, score-0.24]

51 ) , because there are many more irrelevant phrases than relevant ones, i. [sent-773, score-0.212]

52 (4) Thus, our ranking function actually computes the relevance score The last term, log ? [sent-822, score-0.302]

53 Precisely, we want to analyze the coverage of our proposed ranking based on relevance models. [sent-944, score-0.29]

54 the proposed relevance ranking, we want to get an estimate of how many relevant terms from a sample of the collection were covered. [sent-953, score-0.296]

55 Finally, a term was considered to be relevant if both judges marked it so. [sent-960, score-0.231]

56 We then computed the coverage to see how many of the relevant terms in the random sample were also present in top k phrases from the ranked candidate n-grams. [sent-961, score-0.375]

57 4 No agreement (κ < 0), slight agreement (0 < κ ≤ 0. [sent-964, score-0.356]

58 8 < κ ≤ coverage results below moderate agreement (0. [sent-969, score-0.228]

59 We find that choosing top k = 5000 candidate ngrams based on our proposed ranking, we obtain a coverage of 87% for agreement and 89. [sent-979, score-0.365]

60 Thus, we choose top 5000 candidate n-grams for each expression type and add them to the vocabulary beyond all unigrams. [sent-981, score-0.203]

61 However, for topics, selecting k based on coverage of each topic is more difficult because we induce 50 topics and it is also much more difficult to manually find relevant topical phrases in the sampled data as a topical phrase may belong to more than one topic. [sent-993, score-0.839]

62 We selected top 2000 ranked candidate phrases for each topic using ? [sent-994, score-0.318]

63 Note that phrases for topics are not as crucial as for AD-expressions because topics can more or less be defined by unigrams. [sent-1001, score-0.317]

64 5 Classifying Pair Interaction Nature We now determine whether two users (also called a user pair) mostly agree or disagree with each other in their exchanges, i. [sent-1002, score-0.339]

65 However, above works do not discover pair interactions (arguing nature) in debate authors. [sent-1032, score-0.318]

66 We first evaluate the discovered AD-expressions by comparing results with and without using the phrase ranking method in Section 4, and then evaluate the classification of interaction nature of pairs. [sent-1091, score-0.446]

67 For each post, we extracted the post id, author, domain, ids of all posts to which it replies/quotes, and the post content. [sent-1097, score-0.309]

68 The reduced dataset consists of 1095586 tokens (after n-gram preprocessing in §4), 40102 posts with an average of 27 posts or interactions per pair. [sent-1101, score-0.411]

69 3) appearing at least 10 times and labeled them as topical (361) or AD-expressions (139) and used the corresponding features of each term (in the context of posts where it occurs, §3) to train the Max-Ent model. [sent-1126, score-0.415]

70 , Web search results, aspect terms in topic models for sentiment analysis (Zhao et al. [sent-1148, score-0.421]

71 , the Dirichlet smoothing effect ensures that every term in the vocabulary has some nonzero mass to agreement or disagreement expression type. [sent-1159, score-0.659]

72 560 … … …, Table 5: Results using all tokens (without applying phrase relevance ranking) for P@50, 100, 150 and 500 labeled examples were used for Max-Ent (ME) training). [sent-1180, score-0.247]

73 We also studied interrater agreement using two judges who independently labeled the top n terms as correct or incorrect. [sent-1188, score-0.465]

74 78 for all p@n computations implying substantial and good agreements as identifying whether a phrase implies agreement or disagreement or none is an easy task. [sent-1196, score-0.53]

75 P@n excluding ME labeled terms (Table 4, second column) are slightly lower than those using all terms but are still decent. [sent-1197, score-0.225]

76 Further to evaluate the sensitivity of performance on the amount of labeled terms for Max-Ent, we computed p@n across different sizes of labeled terms. [sent-1199, score-0.207]

77 Table 4 shows p@n for agreement and disagreement expressions across different sizes of labeled terms (L). [sent-1200, score-0.71]

78 The result in Table 4 uses relevance ranking (§4). [sent-1203, score-0.24]

79 Clearly, P@n is lower than in Table 4 (last row; with phrase relevance ranking) because without phrase relevance ranking (Table 5) many irrelevant terms can rank high due to co- occurrences which may not be semantically related. [sent-1206, score-0.605]

80 This shows that relevance ranking of phrases is beneficial. [sent-1207, score-0.321]

81 3 Pair Interaction Nature We now evaluate the overall interaction nature of each pair of users. [sent-1209, score-0.275]

82 The evaluation of this task requires human judges to read all the posts where the two users forming the pair have interacted. [sent-1210, score-0.418]

83 Two human judges were asked to independently read all the post interactions of 500 pairs and label each pair as overall “disagreeing” or overall “agreeing” or “none”. [sent-1213, score-0.364]

84 Pairs were finally labeled as agreeing or disagreeing if both judges deemed them so. [sent-1221, score-0.491]

85 This resulted in 320 disagreeing and 152 agreeing pairs. [sent-1222, score-0.326]

86 Out of the rest 28 pairs, 10 were marked “none” by both judges while 18 pairs had disagreement in labels. [sent-1223, score-0.414]

87 We only focus on the 472 agreeing and disagreeing pairs. [sent-1224, score-0.326]

88 As we have labeled data for 472 pairs, we can treat identifying pair arguing nature as a text classification problem where all interactions between a pair are merged in one document representing the pair along with the label given by judges: agreeing or disagreeing. [sent-1225, score-0.861]

89 ), we experiment with top 1000 and 2000 AD-expressions terms for both agreement and disagreement. [sent-1235, score-0.3]

90 comparison results using 5-fold Cross Validation (CV) with two classes: agreeing and disagreeing in Table 6. [sent-1237, score-0.326]

91 Predicting agreeing arguing nature is harder than that of disagreeing across all feature settings. [sent-1243, score-0.585]

92 yields the best performance showing that the discovered AD-expressions are of high quality and reflect the user pair arguing nature well. [sent-1248, score-0.526]

93 7 Conclusion This paper studied the problem of modeling user pair interactions in online discussions with the purpose of discovering the interaction or arguing nature of each author pair and various ADexpressions emitted in debates. [sent-1257, score-1.096]

94 A novel technique was also proposed to rank n-gram phrases where relevance based ranking was used in conjunction with a semi-supervised generative model. [sent-1258, score-0.375]

95 Staying informed: supervised and semi-supervised multi-view topical analysis of ideological perspective. [sent-1282, score-0.251]

96 The power of negative thinking: Exploiting label disagreement in the min-cut classification framework. [sent-1298, score-0.312]

97 Identifying agreement and disagreement in conversational speech: Use of Bayesian networks to model pragmatic dependencies. [sent-1365, score-0.49]

98 Aspect and sentiment unification model for online review analysis. [sent-1403, score-0.228]

99 Topic sentiment mixture: modeling facets and opinions in weblogs. [sent-1453, score-0.252]

100 Predicting response to political blog posts with topic models. [sent-1563, score-0.374]


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tfidf for this paper:

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