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 . Online discussion / debate forums allow people with common interests to freely ask and answer questions , to express their views and opinions on any subject matter , and to discuss issues of common interest . A large part of such discussions is about social , political , and religious issues . On such issues , there are often heated discussions / debates , i.e. , people agree or disagree and argue with one another . Such ideological discussions on a myriad of social and political issues have practical implications in the fields of communication and political science as they give social scientists an opportunity to study real-life discussions / debates of almost any issue and analyze participant behaviors in a large scale . In this paper , we present such an application , which aims to perform fine-grained analysis of user-interactions in online discussions . 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 . , 2003 ) , and classifying user stances ( Somasundaran and Wiebe , 2009 ) . 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 . In this work , we want to mine the following information : 1 . The nature of interaction of each pair of users or participants who have engaged in the discussion of certain issues , i.e. , whether the two persons mostly agree or disagree with each other in their interactions . 2 . What language expressions are often used to express agreement ( e.g. , " I agree " and " you 're right " ) and disagreement ( e.g. , " I disagree " and " you speak nonsense " ) . 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 . They are usually emitted during interactive exchanges of arguments in ideological discussions . This idea prompted us to introduce the concept of ADsentiment . We define the polarity of agreement expressions as positive and the polarity of disagreement expressions as negative . We refer agreement and disagreement expressions as ADsentiment expressions , or AD-expressions for short . AD-expressions are crucial for the analysis of interactive discussions and debates just as sentiment expressions are instrumental in sentiment analysis ( Liu , 2012 ) . We thus regard this work as an extension to traditional sentiment analysis ( Pang and Lee , 2008 ; Liu , 2012 ) . In our earlier work ( Mukherjee and Liu , 2012a ) , we proposed three topic models to mine contention points , which also extract ADexpressions . 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 . Furthermore , we apply the resulting AD-expressions to the new task of classifying the arguing or interaction nature of each pair of users . Using discovered AD-expressions for classification has an important advantage over traditional classification because they are domain independent . 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 . With such complex interactions mined , we can produce many useful summaries of discussions . For example , we can discover the most contentious pairs for each topic and ideological camps of participants , i.e. , people who often agree with each other are likely to belong to the same camp . The proposed framework also facilitates tracking users ' ideology shifts and the resulting arguing nature . The proposed methods have been evaluated both qualitatively and quantitatively using a large number of real-life discussion / debate posts from four domains . Experimental results show that the proposed model is highly effective in performing its tasks and outperforms several baselines . 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 . 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 . This method enables us to find better ADexpressions . Experiments using real-life online debate data showed the effectiveness of the model . In our future work , we intend to extend the model to account for stances , and issue specific interactions which would pave the way for user profiling and behavioral modeling .

Challenge: Existing methods that analyze discussions coarsely leave user interactions with quotes or replies expressing agreements, and what lexicon they use in such cases unstudied.
Approach: They propose to couple a retrieval method with topic modeling to accurately discover ad-expressions to perform a fine-grained analysis of user pair interactions.
Outcome: They applied the proposed method to real-life discussion posts from four domains and quantitative and qualitative evaluation show its effectiveness.