Understanding Interpersonal Conflict Types and their Impact on Perception Classification (2022)

Abstract: Studies on interpersonal conflict have a long history and contain many suggestions for conflict typology. We use this as the basis of a novel annotation scheme and release a new dataset of situations and conflict aspect annotations. We then build a classifier to predict whether someone will perceive the actions of one individual as right or wrong in a given situation. Our analyses include conflict aspects, but also generated clusters, which are human validated, and show differences in conflict content based on the relationship of participants to the author. Our findings have important implications for understanding conflict and social norms.

## introduction understanding social norms is critical to understanding people's actions and intents, not only for humans, but also for artificial agents. the inability for artificial agents to take these norms into account may serve as a barrier to their ability to interact with humans @xcite . however, perceptions of what is socially acceptable behavior vary and issues are often divisive @xcite . it is critical to model these differences both to build higher performing systems and better understand people @xcite . in this work we classify an individual's assessment of conflict situations using the reddit community r/amitheasshole (aita). previous work has examined the classification of social situations involving conflict at both the individual level, and community level (for the aita subreddit). however, it does not consider the types of conflict situations from the perspective of existing conflictfocused literature. we explore methods of clustering descriptions of social situations involving interpersonal conflict and perform a human evaluation and analysis. after proposing a novel annotation scheme, we annotate a set of 500 conflicts with six aspects of conflict. aspects and clusters are then used to provide an analysis of our model performance. we address the task of predicting whether someone will perceive the actions of one individual as right or wrong in a given situation. we hypothesize that, for the prediction model, (1) higher emotional intensity will make predicting the perception of conflict more difficult, (2) when more people are involved, conflict will be harder to assess, (3) the strength of disagreement will not affect prediction difficulty, and (4) that conflict over a longer duration, involving more interference, and that are more manifest than perceived, will be easier to predict, as the additional information gives a clearer picture of the situation and points of discussion. ## related work many classification tasks are subjective in nature. while in some cases it may help to resolve differences between annotators @xcite , it is often insightful to acknowledge and explore the subjectivity of labels assigned by people or groups @xcite . a dataset with labels from individuals, termed descriptive annotations, will help us build models to better understand differences in people's views of socially acceptable behavior @xcite . @xcite first examined aita, suggesting that the descriptive ethics contained in people's judgements could serve as a valuable resource for developing machines that can appropriately and safely interact with people. @xcite further attempted to derive rules-of-thumb from aita to guide ethical reasoning. in contrast, our work classifies how individuals interpret these situations. several recent works have attempted to classify comments, or the judgement that individuals assign in their replies to posts. @xcite examined the classification of both posts and com-ments on aita, finding that posts were more difficult to classify. de candia (2021) found that the subreddits where a user has previously posted can help predict how they will assign judgements and manually classified posts into five categories: family, friendship, work, society, and romantic relationships. more recently, @xcite constructed a comment classifier and used it to study the behavior of users in different subreddits. several of these works have examined characteristics of posts and authors and the judgements they receive, including passive voice, framing, gender, and age @xcite @xcite . interpersonal conflict. distinctions between conflicts can be made based on who is involved. intrapersonal occurs within oneself, while interpersonal occurs between individuals. conflict with more people can occur within or across groups or organizations. much research on the topic has focused on work goals and differentiates between task-related issues and those that result from differences in personality, values, or style @xcite . this work has found it useful to distinguish between conflicts concerning interpersonal incompatibilities and those that arise from the content of a task being performed @xcite . further types have been introduced, though meta-analyses have found these types to be highly correlated and thus researchers have called for improvements to how conflict is conceptualized and measured @xcite @xcite . @xcite surveyed work on interpersonal conflict and noted that studies focused on three common attributes: disagreement, negative emotion, and interference, which correspond to cognitions, emotions, and behaviors respectively. they suggest that these aspects vary across situations and that it is important to specify the target of the conflict. they define interpersonal conflict as "a dynamic process that occurs between interdependent parties as they experience negative emotional reactions to perceived disagreements and interference with the attainment of their goals." as this suggests, conflict is about perception (hussein and al-mamary, 2019). @xcite referred to @xcite 's three attributes as the experience of incompatibility, and suggested two additional considerations; differences in desired outcomes, behaviors, values, or beliefs, and the conflict between and among groups. @xcite further suggested clarifying the intensity of opposition (e.g. fight versus disagreement), specifying conflict duration, and distinguishing between perceived and manifest representations of conflict. these suggestions provided the basis of our annotation scheme described in §4. to our knowledge, no study has yet examined computational approaches to classifying the perception of social situations from the perspective of previous work on interpersonal conflict. ## data we collected data from reddit, an online platform with many separate, focused communities called subreddits. in particular, we use data from the aita subreddit, where members post a description of a social situation involving an interpersonal conflict and ask other members of the subreddit if they think the author of the post is the wrongdoer in the situation or not. others will respond saying "you're the asshole" (yta), or "not the asshole" (nta). as an initial source to crawl the comments, we use the posts from @xcite . we crawl the post title together with its full text, and all the comments that contain a verdict (yta or nta, extracted with a list of variations). our dataset contains 21k posts, and 364k verdicts (254k nta, 110k yta) written in english. to analyze the types of conflicts, we further group posts into distinct categories as described in §5. ## annotation of conflict aspects given the history of the typology of conflict, discussed in §2, we decided to measure six aspects of conflict; (1) strength of disagreement, (2) intensity of negative emotion, (3) degree of interference, (4) duration of conflict, (5) manifestation of conflict, and (6) how many people are involved. aspects 1-3 correspond to the three attributes outlined by @xcite , but with the view of measuring their intensity. @xcite 's suggestions directly inspired aspects 4 and 5 and @xcite 's suggestions about groups led to aspect 6. the authors then annotated a sample of 25 conflicts in order to refine our task. this process made evident how previous conflict scales were not wellequipped for our data. our conflict situations do not always take place in work settings. the nuance of scales like @xcite seemed unnecessary, as conflict is assumed in our setting and as a third party, levels of intensity are less clear (e.g. how to differentiate between degrees of friction, tension, emotional conflict, and personality conflict). longitudinal aspects also cannot often be directly determined. with these insights we refined our annotation questions, which are provided in appendix a. a subset of 500 posts corresponding to 1,653 comments from the test set were provided to annotators. matthews correlation coefficient @xcite ) was used to measure agreement between annotators for 100 posts and is shown in ## clustering before we acquired any annotated data, we performed an exploratory analysis to determine if there was a natural way of grouping conflicts into different types that would be useful for our analysis. we used two representations to perform clustering: situations and all text from the post (full text). situations, as referred to in @xcite , come from the title of a reddit post and serve as a summary of the situation described in the full post. the posts usually start with "aita for", which we omit. we cluster posts using louvain clustering, which maximizes the modularity of our graph @xcite . we create a weighted graph based on each criterion, using situations or full texts as nodes. their embeddings are obtained with sentence-bert (sbert; @xcite ), and use the cosine similarity, normalized to [0, 1] between each pair of nodes as weighted edges, resulting in two fully-connected graphs. the graphs are pruned by dropping the n% lowest edge weights determined by the adjusted rand index between graphs with a 10% difference in the number of dropped edges in order to find a persistent clustering. this yields @xmath1. after clustering each had 3 clusters (see appendix b). manually inspecting the clusters revealed that the groups differ from each other by the social relation of the author to the others in the situation, or how close the author is to others in the situation. for manual verification but also in an effort to explore possible modifications to the groupings, a subset of 100 posts were manually clustered by two of the authors, who intended to form a small number of groups based on the post title and content. while considering other possible groupings both came to the conclusion that it appears most natural to group the posts based on social relation. the events that occur in a conflict, understandably, appear strongly dependent on the relation between participants. upon manual inspection and discussion between annotators, we find that differences arise from two sources. the first is boundaries between social relations. for instance, one annotator grouped family, romantic relationships, and best friends into one cluster, and put all other friends in a second cluster, while the other annotator put family in one cluster and all romantic relationships and friendships in a second. the second source of disagreement comes from perception of who is involved in the conflict. for instance, in one post, a person borrows an object from a family member's friend and although the family member is upset, we do not know if the friend is upset. one annotator saw this as a family conflict, while the other saw it as involving someone more distant. the ari was 0.33 between humans, 0.38 and 0.15 between full text and humans, and 0.31 and 0.13 between humans and situations. we refer to the family cluster and the clusters containing close or more distant individuals in subsequent analyses. examples from each cluster type are shown in appendix e. that higher emotional intensity would be more difficult, as different people may empathize differently and the classification of emotions is known to be a challenging task in itself. when more people are involved in a conflict, we hypothesized that this would be harder for our model to predict. with more involved parties, coreference resolution becomes more challenging and the interaction of more parties may make interpretation of the situational context more complex. however, we thought that the classifier would perform similarly for both mild and strong disagreements, as we did not see why this aspect by itself would make the task more or less challenging. we predicted that it will be easier for the model to predict conflicts that occur over a longer duration, that involve more interference, and that are more manifest than perceived. first, longer duration conflicts may mean that there has been more time to accumulate information about the conflict. in our observations, it also often means that someone is repeating an action. these repeated actions and additional information may give a clearer signal of what facts will lead to a verdict. similarly, with interference, the action is much clearer when interference is high (e.g. someone taking something away from someone, or preventing people from seeing each other). lastly, when conflict is manifest, it means that an annotator decided the conflict was more manifest than perceived by the author. when the conflict is more perceived, the reader has to infer more from the text. for example, the author may think they did something wrong (e.g. not moving in with friend) but the author does not seem to know how the other person feels. ## perception experiments we classify the perception of individuals based on their comments to posts. we concatenate the situation (post title) and comment text after filtering out any labels (e.g. yta). as our base model, we fine-tune sbert on the binary task of predicting the perception of the author, given by a verdict (yta or nta). we also tried using this model to encode the full text to use as additional features, though we found no difference in performance over using only the comment text and situation, which often succinctly captures the event. we compare our model to the recent work of @xcite judgebert, which is a bertbase @xcite model fine-tuned on our dataset, which is extended with a dropout layer and classification layer. judgebert was evaluated in the work from @xcite using a dataset with collections of posts submitted between january 1, 2017, and august 31, 2019, over different subreddits. for the purpose of this work, we reimplemented judgebert in order to evaluate it on our dataset. the main difference between the two models is the encoder layer, where one uses a bert-base model, and the other one a sbert model. we train both models for 10 epochs, using the adam optimizer, learning rate of 1e -4 and focal loss @xcite to cope with class imbalance. we split our dataset into 70-20-10 for training, validation, and test, respectively. we stratify in two ways, for each clustering method. the results are reported in we further break down our results by conflict aspects in ## discussion overall, our model outperforms previous work for our full data and for each cluster. as noted in §2, it is important to understand the subject of the conflict, though in our work we found that this was highly coupled with the type of relation between participants. future work may consider ways of separating these concepts. if one considers the family cluster as the most close social relationship, we find an indirect relationship between the closeness of participants in a conflict and the difficulty in classifying perceptions of that conflict. the closeness of relation to conflict participants, strength of negative emotions and opposition, duration of the conflict, manifestation, and the number of people involved all impact on our classifier's ability to classify people's perception of social norms. these findings pertain to the understanding of conflict, behavior, and personal narratives, but may prove useful for other tasks such as argumentation, framing detection, and understanding offensive speech. ## conclusions we developed a novel annotation scheme for aspects of conflict and built a classifier to predict individual people's perception of right and wrong. our analysis with the aspects and generated clusters showed that the closeness in social relation between people in conflict, strength of disagreement and negative emotion, conflict duration, manifestation, and the number of people involved all impact the difficulty of predicting personal perceptions. future work on language understanding and social norms should consider the impact of these aspects. our code and dataset containing 21k posts, 364k comments, two sets of cluster labels, and our 500 posts labeled with the six conflict aspects, corresponding to 1,653 verdicts is available on our github. foot_1