Examining Gender and Power on W ikipedia through Face and Politeness (2024)

Abstract: We propose a framework for analyzing discourse by combining two interdependent concepts from sociolinguistic theory: face acts and politeness. While politeness has robust existing tools and data, face acts are less resourced. We introduce a new corpus created by annotating Wikipedia talk pages with face acts and we use this to train a face act tagger. We then employ our framework to study how face and politeness interact with gender and power in discussions between Wikipedia editors. Among other findings, we observe that female Wikipedians are not only more polite, which is consistent with prior studies, but that this difference corresponds with significantly more language directed at humbling aspects of their own face. Interestingly, the distinction nearly vanishes once limiting to editors with administrative power.

## related work the theory of politeness of b&l has found applications in many fields including sociology, psychology, and linguistics. google scholar lists nearly 38,000 citations. curiously, in nlp there has not been much work building explicitly on b&l. @xcite concentrate on one type of face-threatening act (fta), namely the negative face-threatening act of a request, and investigate the strategies used for doing this fta. to do this, they use crowd sourcing to rate the requests on a politeness scale. they develop a model which predicts the politeness of these requests and use it to study the interactions between users on wikipedia and stackexchange. @xcite show that fine-tuning on the data of @xcite substantially outperforms zero-shot approaches. the face acts (fas) themselves are the object of @xcite . in addition to developing a dataset annotated with fas, they present a fa classifier based on a neural architecture they devise on top of bert, which achieves 69% f-measure (0.60 macro). as the data involves participants convincing others to donate to a charity, they also use this corpus to investigate the relationship between face acts and persuasion by predicting if a participant chose to donate. this corpus, which we refer to as the "cmu face acts corpus" (or "cmu corpus" for short) in this paper, is the direct inspiration for our annotation effort on the wikipedia data. we differ from their annotation scheme in some important details; we present our annotation in §4. in prior work, we investigated the interaction of intention (through dialog act tagging) and face acts in the cmu corpus @xcite . there has been an explosion work in compu-tational social science in general, in which nlp tools are used to extract relevant signals from large amounts of data in order to study a social phenomenon, such as changing attitudes towards certain topics as expressed on social media. for an overview, see @xcite . in the area of studying how gender and power shape written dialogs, there has been some work in nlp. working with corporate emails, @xcite find that gender differences become exaggerated when looking at individuals with greater social power; specifically, among people with power, women behave more differently from men than when comparing people without power. finally, turning to the study of politeness and gender outside of nlp, there have been some studies based on manual analysis of collected data, for example @xcite @xcite . for space reasons, we discuss only one example in more detail. @xcite , using a framing approach following @xcite , finds that women in power who "downplay status differences (...) are exercising and constituting their authority by speaking in ways that accomplish work-related goals while maintaining the faces of their interlocutors". in the terminology of b&l (which @xcite does not use), women perform similar face acts to men but use strategies to mitigate the effects, which results in women in power appearing more polite than men in power. ## theoretical framework in this section we provide a brief summary of relevant concepts from politeness theory as it relates to our work. our goal in this paper is to explore how face acts contribute to the perception of politeness. for b&l, "face" refers to the public self-image of agents, and it is a universal component of human interaction. it consists of two complementary facets @xcite . (1) negative face: "the basic claim to territories, personal preserves, rights to non-distraction -i.e. to freedom of action and freedom from imposition." (2) positive face: "the positive consistent self-image or 'personality' (crucially including the desire that this self-image be appreciated and approved of) claimed by interactants." a face act is an intentional communicative act which inherently interacts with the face of the speaker and/or addressees @xcite affirm (+) the face; they can be about the speaker's face (s) or the hearer's (h); and they can be about positive (pos) or negative (neg) face. this gives us eight possible face acts, shown in both utterances are hneg-/imposition face acts, because they impose on the hearer the obligation to respond. however, (1) rejects the previous question by b and challenges b, while (2) is just a request for information, so that (1) is perceived as more impolite than (2). it is possible for a single utterance to perform multiple face acts at once. for example, (1) could also be seen as disagreement, since it entails a critique of b's actions. however, @xcite observed multi-labeled acts in only 2% of their data, leading them to consider a single label per utterance. we make this simplification as well in the work presented in this paper. ## face act tagging in this section we outline the data, modeling techniques, and evaluation measures used in developing our face act tagger for wikipedia talk pages. ## application and analysis we apply our new face act tagger along with the politeness scores provided by convokit @xcite to study the interactions of face and polite- our face act tagger is trained using our entire annotation ( §4.2) before applying it to the wikipedia data. this produces roughly 1.3 million sentences labeled with face acts and perceived politeness. we note that the politeness scores are obtained for the entire turn, as this is what the perceived politeness model is trained on, while face acts are tagged by sentence to allow for greater granularity. in our analysis of politeness we investigate how polite (magnitude) editors are perceived to be by looking at their scores and how often that occurs (frequency) by considering the proportion of utterances in the top 25% of politeness scores. for face acts, we compare the overall distribution (frequency) of labels. statistical significance is calculated using the mann-whitney u test. this analysis was also performed on only the human annotated portion of the data and the trends remained consistent. we report results on the entire corpus. ## conclusion and future work we identify an optimized method for training face act taggers using fine-tuning on llms, contribute a new corpus annotated for face acts, and make available a pre-trained model for use on wikipedia. through several methods of analysis we demonstrate the usefulness of examining perceived politeness in combination with face acts by reporting a number of findings based on their interaction. in future work we plan to allow multiple face acts per utterance (including for the same segment), and to incorporate the strategy (as conceived of by b&l) more explicitly into our modeling framework.