We identify agreement and disagreement between utterances that express stances towards a topic of discussion. Existing methods focus mainly on conversational settings, where dialogic features are used for (dis)agreement inference. We extend this scope and seek to detect stance (dis)agreement in a broader setting, where independent stance-bearing utterances, which prevail in many stance corpora and real-world scenarios, are compared. To cope with such non-dialogic utterances, we find that the reasons uttered to back up a specific stance can help predict stance (dis)agreements. We propose a reason comparing network (RCN) to leverage reason information for stance comparison. Empirical results on a well-known stance corpus show that our method can discover useful reason information, enabling it to outperform several baselines in stance (dis)agreement detection.
Our work is mostly related to the task of detecting agreement and disagreement in online discussions. Recent studies have mainly focused on classifying (dis)agreement in dialogues @cite_17 @cite_19 @cite_4 @cite_14 . In these studies, various features (e.g., structural, linguistic) and or specialised lexicons are proposed to recognise (dis)agreement in different dialogic scenarios. In contrast, we detect stance (dis)agreement between independent utterances where dialogic features are absent.
@cite_19 @cite_14 @cite_4 @cite_17
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We study the problem of agreement and disagreement detection in online discussions. An isotonic Conditional Random Fields (isotonic CRF) based sequential model is proposed to make predictions on sentence- or segment-level. We automatically construct a socially-tuned lexicon that is bootstrapped from existing general-purpose sentiment lexicons to further improve the performance. We evaluate our agreement and disagreement tagging model on two disparate online discussion corpora ‐ Wikipedia Talk pages and online debates. Our model is shown to outperform the state-of-the-art approaches in both datasets. For example, the isotonic CRF model achieves F1 scores of 0.74 and 0.67 for agreement and disagreement detection, when a linear chain CRF obtains 0.58 and 0.56 for the discussions on Wikipedia Talk pages. Casual online forums such as Reddit, Slashdot and Digg, are continuing to increase in popularity as a means of communication. Detecting disagreement in this domain is a considerable challenge. Many topics are unique to the conversation on the forum, and the appearance of disagreement may be much more subtle than on political blogs or social media sites such as twitter. In this analysis we present a crowd-sourced annotated corpus for topic level disagreement detection in Slashdot, showing that disagreement detection in this domain is difficult even for humans. We then proceed to show that a new set of features determined from the rhetorical structure of the conversation significantly improves the performance on disagreement detection over a baseline consisting of unigram bigram features, discourse markers, structural features and meta-post features. Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community’s ability to develop better theories, as well as good off-the-shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic-independent way. Our results show that our theoretically motivated features achieve 66 accuracy, an improvement over a unigram baseline of an absolute 6 . The recent proliferation of political and social forums has given rise to a wealth of freely accessible naturalistic arguments. People can "talk" to anyone they want, at any time, in any location, about any topic. Here we use a Mechanical Turk annotated corpus of forum discussions as a gold standard for the recognition of disagreement in online ideological forums. We analyze the utility of meta-post features, contextual features, dependency features and word-based features for signaling the disagreement relation. We show that using contextual and dialogic features we can achieve accuracies up to 68 as compared to a unigram baseline of 63 .