Detecting Perspective-Getting in W ikipedia Discussions (2024)

Abstract: Perspective-getting (i.e., the effort to obtain information about the other person’s perspective) can lead to more accurate interpersonal understanding. In this paper, we develop an approach to measure perspective-getting and apply it to English Wikipedia discussions. First, we develop a codebook based on perspective-getting theory to operationalize perspective-getting into two categories: asking questions about and attending the other’s perspective. Second, we use the codebook to annotate perspective-getting in Wikipedia discussion pages. Third, we fine-tune a RoBERTa model that achieves an average F-1 score of 0.76 on the two perspective-getting categories. Last, we test whether perspective-getting is associated with discussion outcomes. Perspective-getting was not higher in non-escalated discussions. However, discussions starting with a post attending the other’s perspective are followed by responses that are more likely to also attend the other’s perspective. Future research may use our model to study the influence of perspective-getting on the dynamics and outcomes of online discussions.

## introduction understanding what other individuals think or feel is important for effective interpersonal behavior, such as building and maintaining social relationships @xcite , effective customer service @xcite , or reaching agreements in negotiations @xcite . individuals take another individual's perspective (i.e., engage in perspective-taking) by putting themselves in the same "mental shoes" as the other @xcite and by recognizing that this other person's perspective differs from their ego-centric perspective @xcite . scholars have shown that imagining someone else's perspective and incorporating this alternative viewpoint in communication is a notoriously difficult task @xcite @xcite . individuals may take their own perspective as an anchor for assessing other's perspective, lack motivations to seek for information about other's perspective, or consider perspectivetaking as requiring too much mental effort @xcite . consequently, individuals often fail to accurately infer the perspective of others @xcite . in contrast to the cognitive process of imagining an other's perspective through perspectivetaking, the strategy of perspective-getting results in a more accurate assessment of the other's perspective @xcite @xcite . perspective-getting refers to directly asking about and listening to information related to another person's thoughts, feelings, beliefs, or preferences @xcite @xcite instead of simply imagining the other's perspective. in contrast to perspective-taking, perspectivegetting can likely be more accurately annotated and detected as it is about detecting visible behavior as opposed to cognitive states from text. perspectivegetting has been operationalized as whether the interlocutor asks a target interlocutor about their perspective @xcite and as whether interlocutors summarize the other's perspective @xcite . previous work manipulated perspective-getting via participant instructions in experimental settings @xcite @xcite . to the best of our knowledge, perspective-getting was not studied outside lab settings and has not been investigated computationally. we study perspectivegetting in a natural online discussion settings on wikipedia and build a classifier for computational analysis. ## background perspective-getting @xcite @xcite has been a more recent term to differentiate the cognitive process of perspective-taking from strategies that aim at "getting" the perspective from another person (e.g., directly asking about their perspective). this understanding of perspective-getting has previously been and sometimes still is incorporated in some definitions of perspective-taking @xcite @xcite . in this work, we focus on perspective-getting but we include insights from "perspective-taking" studies if they operationalize it similar to our understanding of perspective-getting as defined in section 3. perspective-getting has not yet been measured using nlp methods. but our two categories for perspective-getting (aop and afc, see also asking for clarification (afc) is related to detecting questions, which can be challenging in online messages as, for example, question marks can be absent or questions might be phrased as information requests @xcite . transformerbased architectures have been used with moderate success to detect questions in informal online texts @xcite @xcite . for clarification questions specifically, there exist corpora @xcite and generative models @xcite . however, our problem is even more complex than detecting clarification questions. we are interested in questions aimed at understanding the other's perspective. some "clarification questions" might not aim to do that (e.g., "you're joking, right?" in our categories have also been relevant in other work: for example, afc and paraphrasing/summarizing operationalized in the aop category have been found to be critical to establish a common ground of shared knowledge @xcite @xcite and increasing understanding of each other @xcite . a related task is empathy detection @xcite @xcite @xcite @xcite . for example, @xcite predicted the empathy level of therapists in motivational interviews and defined empathy as: "the extent to which the therapist understands and/or makes an effort to grasp the client's perspective". furthermore, @xcite developed a framework to measure expressed empathy in online mental health dialogs. two categories, explorations (acquiring more information about others' internal states) and interpretations (expressing understanding of states of others), relate to perspective-getting but are still too far removed from the setting of online discussions. based on (1) and (2), we introduce a codebook (see appendix a) for the text-based detection of perspective-getting with two distinct categories: asking for clarification of the other's perspective (afc) or attending to the other's perspective (aop), for example, through summarization. examples of messages displaying each of the two perspective-getting categories can be found in ## dataset wikidisputes we use our codebook to annotate perspective-getting in the wikidisputes dataset from de @xcite . this dataset is derived from wikipedia talk pages, where editors discuss possible changes to wikipedia articles. wikidisputes consists of 133,019 messages in nonescalated discussions and 4,441 messages in escalated discussions, i.e. where participants asked for the discussion to be mediated by a moderator. when editors do not reach consensus, the conflict is escalated and resolved by arbitration. 1 previous research shows that when task-related conflicts escalate, it decreases the group performance related to editing the wikipedia page @xcite . ## predicting perspective-getting we now use our annotated dataset to train and test classification models to predict the two perspectivegetting categories. ## limitations first, annotation was sometimes challenging due to characteristics of our data. some replies were intended for a different user, such as the opening poster of the discussion, rather than the user who was directly replied to, which could affect the annotation of aop. furthermore, users sometimes added links to the text with the 'insert link' function. these links, however, are not visible in the dataset, which could affect the annotation of the psa control variable, as links could lead to sources that do not support one's position. second, perspective-getting can be operationalized in a more fine-grained way than our binary categories. for example, just acknowledging a perspective (e.g., "i understand that your view is x. but here is what i think ...") could be seen as less aop than actually engaging with it (e.g., "it seems the point you are making is x. i understand how this might come from the belief that ..."). third, the number of annotated messages is relatively low. due to the complexity of perspectivegetting, we opted for careful annotation by expert annotators instead of item quantity. due to the small number of escalated discussions in the original dataset, the number of escalated discussions is especially small (c.f. fourth, the generalizability of our study is limited, as perspective-getting might manifest differently in other domains. for example, wikipedia editors have to come to an agreement, while so-cial media users discussing presidential candidates do not. further, most replies in our dataset will in some form disagree with the previous message, including those with high perspective-getting. still, our codebook was developed based on theoretical work and should be transferable to other settings, possibly with some minor changes. fifth, our data sample is in english and is likely skewed to specific demographics. our classifiers are therefore likely to be biased towards these same demographics in performance. furthermore, our classifiers could latch on to spurious features. we do not recommend using the classifier "out of the box" on datasets that are not comparable. ## conclusion in this study, we measured perspective-getting in wikipedia discussions. based on theory, we developed a codebook to annotate messages in wikipedia discussions on two perspective-getting categories, and then fine-tuned a roberta model to predict the categories. in our dataset, perspectivegetting was not higher in non-escalated discussions. however, discussions starting with a post attending the other's perspective are more often followed by responses that also attend to other's perspective. with this work, we hope to contribute to studies of how and when strategies for interpersonal understanding can reduce polarization in online discussions @xcite , as perspective-getting allows people to understand, consider and adopt the opposite position in discussions @xcite @xcite . future research could build on our work to study the relation between perspective-getting and online polarization, while considering the contextual and causal limitations of our study.