How-to Guides for Specific Audiences: A Corpus and Initial Findings (2023)

Abstract: Instructional texts for specific target groups should ideally take into account the prior knowledge and needs of the readers in order to guide them efficiently to their desired goals. However, targeting specific groups also carries the risk of reflecting disparate social norms and subtle stereotypes. In this paper, we investigate the extent to which how-to guides from one particular platform, wikiHow, differ in practice depending on the intended audience. We conduct two case studies in which we examine qualitative features of texts written for specific audiences. In a generalization study, we investigate which differences can also be systematically demonstrated using computational methods. The results of our studies show that guides from wikiHow, like other text genres, are subject to subtle biases. We aim to raise awareness of these inequalities as a first step to addressing them in future work.

## introduction how-to guides provide practical instructions that help humans to achieve specific goals. in the past decades, such guides also attracted increasing interest in nlp and ai research @xcite @xcite . resources such as wikihow, 1 a collaboratively edited online platform for instructional texts, make it possible to scale research efforts to hundreds of thousands of articles. by covering an ever-increasing number of guides, including niche topics and articles for minority groups, there is also an increasing risk of perpetuating stereotypes and jeopardizing general accessibility. in fact, we notice that wikihow already contains articles written for specific target groups as well as articles that exist in different versions for different audiences. as an example, among other things, we find that such articles dramatically differ in terms of details. for example, the texts highlighted in there already exists a large body of research that examines biases and stereotypes in nlp data and, likewise, how-to guides from wikihow have been used as training material for a variety of language processing tasks ( §2). however, previous studies have not explicitly looked into issues related to bias in the wikihow data. as a first step towards addressing this gap, we create our own sub-corpora of how-to guides, which let us investigate differences across articles for specific target groups ( §3). we perform two case studies and a generalization study on our collected data: in the first study, we identify a number of articles that exist in multiple variants for different target groups and examine them in terms of distinctive content and linguistic characteristics ( §4). as a second case study, we explicitly examine how far topics covered for specific target groups differ from each other ( §5). finally, we investigate whether the qualitative findings from our case studies can be validated quantitatively and generalized to our whole corpus using computational modeling ( §6). in summary, we find systematic differences between articles for specific groups in terms of topic, style, and content. we conclude the paper with a discussion of these findings and point out links to existing work in the social sciences ( §7). ## related work we summarize existing work on the three strains of research that this paper builds on: wikihow as a data source ( §2.1), subtle biases in datasets ( §2.2), as well as understanding the characteristics of texts that target specific audiences ( §2.3). ## corpus construction as introduced in §2.1, wikihowtoimprove is a well-established data set derived from wikihow and consisting of more than 246,000 how-to guides. in general, each guide consists of multiple revisions of an article, a fixed goal that is named in the title, and (optionally) an indicator that follows the title in parentheses (cf. based on a manual grouping of these indicators, we find that 15 out of 20 indicators refer to attributes of performative gender and age (the remaining five are underlined in ## case study: same title, different audience therefore, we start our investigation by analyzing how often such cases occur in wikihowaudiences, which topics they cover, and what differs between versions for specific target groups. ## case study: "how to be" guides in the previous section, we looked at how-to guides that occur in different versions for specific audiences. such guides might concern particular goals that require being addressed in distinct ways. in this section, in contrast, we broaden the scope of analysis to explore other cases of differences in audience-specific instructions. ## generalization study: computational approach our case studies show that, depending on the audience, there exist examples of articles that differ in terms of topic, length, style, and/or vocabulary. however, an open question is whether these are only individual cases or if such differences occur systematically. in this study, we investigate this question computationally and attempt to verify our observations on the basis of a larger dataset. for this purpose, we implement tentative characteristics in the form of features and models ( §6.1), evaluate in a setting with our full sub-corpora ( §6.2), discuss quantitative results ( §6.3), and analyze qualitative findings ( §6.4). ## discussion and conclusion in this paper, we assessed differences across howto guides written for specific audiences. in the construction of sub-corpora for four target groups, we already noticed inequalities on the level of who is being instructed in wikihow: as a target audience, women are mentioned more than four times more frequently than men, and teens receive about 50% more instructions per article than kids. in two case studies, we investigated and provided examples of target-related differences on the levels of topic, style, and content. the differences observed in our case studies inspired feature sets of shallow classifiers for predicting the target audience of a given guide. using these classifiers, we showed that it is, in many cases, indeed possible to automatically predict for which audience an article was written. in an analysis of our results, we found that this success is not merely based on different topics covered for each target group but that the articles for each group systematically differ in terms of content and style. each of the aforementioned observations presents a tiny, seemingly insignificant piece of a puzzle. but taken together, these pieces reveal a surprisingly clear picture: there are noticeable differences in what topics are covered for each target group, how many articles and instructions are provided for each audience, and how these articles are written. even though the audience-specific characteristics used in our studies are by no means exhaustive, our straightforward approach allowed us to identify, qualitatively and quantitatively, debatable differences in how wikihow guides present particular topics to specific target groups. while there is an inevitable need for differences in vocabulary when speaking about physical features or body parts, it is at best unclear in which ways how-to guides about human interactions or selfpresentation should cast significant differences. some of the observed differences have already been critically discussed in the context of social science research. for example, it is well-known that labels such as 'cute' are used pejoratively as a form of social control @xcite and that prescriptive components of gender stereotypes in education contribute to discrimination @xcite . however, exposing readers to cultural messages and beliefs about age, gender or other factors cannot be avoided entirely, especially on a collaboratively edited online platform. in fact, it seems to be a challenge for any pluralistic society to find a balance between communicating traditional values and empowering everyone. it is therefore all the more important for a comprehensive understanding to determine when and in what form social norms are conveyed. as such, we view the contributions of this paper, namely our data set of audience-specific guides, wikihowaudiences, and our mixed-methods approach for identifying and verifying differences, as a valuable connecting point to raise awareness of potential issues and to foster interdisciplinary dialogue for future research.