Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual W ikipedia Editor Discussions (2023)

Abstract: The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and editors are encouraged to use the content moderation policies as explanations for making moderation decisions. Currently, only a few comments explicitly mention those policies – 20% of the English ones, but as few as 2% of the German and Turkish comments. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. The dataset contains the stances of the editors (keep, delete, merge, comment), along with the stated reason, and a content moderation policy, for each edit decision. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process. We release both our joint prediction models and the multilingual content moderation dataset for further research on automated transparent content moderation.

## introduction moderators typically discuss content moderation decisions they want to take collectively. in those discussions, it is crucial to understand the stances of the discussion participants. as moderators often refer to content moderation policies @xcite , these can be seen as a justification for their proposed solution for the problem at hand. therefore, understanding the stance of moderators and how they come to a decision is crucial for community members to ensure that the decisions are made based on the policies the community has agreed on. it has previously been utilised, using a wisdom-of-the-crowds approach, for moderation in specific scenarios, for instance, factchecking @xcite . moderator decisions, such as changing the status of a subreddit to private in protest @xcite , is typically a decision made in dialogue with the community and based on their feedback and previously agreed upon policies for that community. as stance detection is a crucial task for understanding the reasoning for content moderation decisions, we here propose a method to explain stances with existing moderation policies. policies in general can take various shapes and forms @xcite . for many online platforms, these policies are hidden in terms and conditions documents and point to what is permissible or not on a platform, typically to mitigate potential harms @xcite . for others, they are explicitly stated for the community to refer to, where these policies should be included in the considerations when content is discussed @xcite . communities on those platforms further have norms and policies of their own @xcite . for members of such communities, it is imperative to be able to comprehend why their content was deleted based on these policies. for the platforms themselves, highlighting such policies are crucial for maintaining the trust and safety of the platform and is also required by upcoming regulation @xcite . finally, for the content moderators on those platforms, navigating through a large number of legalese-heavy policies can be challenging and time-consuming. however, to the best of our knowledge, supporting content moderators with automated tools that jointly predict a moderator's decision and the corresponding policy has not been explored in prior research. such a discussion process referring to policies exists, for example, on wikipedia, a communityedited encyclopedia. here, policies ensure the qual-ity of the written articles and that standards are kept. for example, the manual of writing describes how an article should be written, the reliable sources policy ensures that enough and high-quality references are included in an article, and the notability policy ensures that only topics notable enough for an encyclopedic entry are included on wikipedia. some form of these policies exists across different language wikipedias, ensuring that the respective community standards in each language are clearly communicated and upheld. if an article does not live up to the communities' standards, editors on wikipedia can recommend the article for deletion. these deletion discussions are a form of content moderation process, in which editors cite established policies to argue for the deletion of, e.g., a group or post. across all wikipedia language versions, the process is very similar: an article is suggested for deletion, a deletion discussion takes place in which the editors support or oppose the deletion of the article, or propose to merge it with other articles or comment on the discussion. for an example comment in such a deletion discussion, see figure 1 . eventually, an admin on wikipedia takes action using the input of the discussion. a variety of guidelines are developed to help editors make this decision. 2 when discussing the deletion of articles, many editors refer to existing policies in wikipedia, to base their arguments on these evidence documents. in discussions, referring to evidence documents is helpful, as it grounds arguments in more objective points and previously agreed-upon standards @xcite . automating the task of getting an overview and proposing policies to cite is crucial to help admins make consistent decisions, and for users to understand how and based on which policies decisions are discussed. currently, there is limited work in the area of transparent content moderation, and none of it has considered policies in community discussions as explanations or reasoning for stance. we therefore propose a multi-task setup in which stance is detected alongside the prediction of a justification in the form of a policy, as described in figure 2 . as different language wikipedias have different sizes in terms of articles and accordingly deletion discussions, we further propose a multilingual setup, aligning policies across different to this end, our contributions are as follows: ## related work policies for content moderation online content moderation and, with it, the policies that moderators use are indispensable on the current online platforms @xcite . @xcite describe that once a community grows, its implicit rules need to be formalised into standards, guidelines, or policies. social media platforms, such as facebook, have policies to aid content moderators in deleting or keeping posts of users. conflict arises when those policies are applied non-transparently @xcite . as content moderation is a crucial topic, there have been proposals to automate aspects of it. @xcite propose an automated method to suggest the deletion of social media comments based on general guidelines, such as insults, discrimination, and defamation. they do however not analyse the discussion of content moderators themselves. @xcite suggest that automated content moderation could increase adherence to community standards. in our paper, we focus on the policies used in wikipedia. they are created and maintained by community members, as any wikipedia page can be edited by any user. these policies play an important role in negotiating community standards and have a wide range of functions @xcite . to the best of our knowledge, there is no prior work on automatically detecting policies in content moderator discussions as a justification for their decisions. deletion discussion on wikipedia xiao and askin (2014); @xcite find that in deletion discussions, rationales stated by the editors are rooted in established policies. one of the important aspects for editors contributing to the deletion discussions is the familiarity with the norms of these discussions @xcite , in which citing existing policies can be of great help. the deletion discussions have been analysed in an automated manner to understand how online communities communicate. @xcite detect sentiment in the deletion discussions on english wikipedia and find that positive sentiments tend to indicate an article being kept, and negative sentiments tend to indicate that the article will be deleted. @xcite model wikipedia deletion discussions as group decision-making processes. to this end, they use stance detection to evaluate each user's attitude. they show the importance of understanding stance in the wikipedia deletion discussions. however, they only work on a subset of the deletion discussions in the english wikipedia. @xcite propose a method to identify online user arguments in need of support, but do not take the next step of proposing supporting documents. ## transparent stance detection in content moderation, obtaining an overview of the attitudes of different moderators towards a decision is crucial to reach a consensus. the reasoning of the proposed decisions by moderators is ideally grounded in agreed upon community standards. in wikipedia, deletion discussions are a forum for exchanges about whether an article should be deleted or kept on the platform. in our work, we aim to support the admins, who make the final decision on whether an article should be deleted or kept, by automating the process of getting an overview of the different attitudes. given a comment on a topic, such as the example comment in figure 1 , we aim to predict the stance of the user along with their reasoning in form of a policy, as displayed in figure 2 . the task of transparent stance detection consists of two subtasks: 1) policy prediction, in which we predict the reasoning for a decision in the form of a policy; and 2) stance detection, in which the stance of the comment is detected. transparent stance detection aims to predict the policy and stance of a comment alongside each other. we explore these tasks in a multilingual manner, leveraging knowledge from high-resource language wikipedias to improve task performance on lower resourced ones. ## dataset we create three new datasets, containing annotations of stances and policies in articles for deletion discussions in the english, german, and turkish wikipedias. the datasets cover the time span from 2005 (2006 for turkish) to 2022. each instance in the dataset consists of the title of the page that is discussed for deletion as the topic, the stance towards the topic, i.e. deletion of the article, the comment of the user arguing for or against deletion, and the policy they cite to justify their argument. an example comment, shown in figure 1 , is "[[delete]] blatant advertising. fails @xcite ]. the whole article reads like an advertisement", where delete is the stance of the editor and the mentioned policy is notability. more examples from the dataset can be found in the appendix in ## experimental setup we consider four setups for our evaluation of individual and joint prediction of stance and policy: i) single task where we consider the tasks outlined in section 3 individually while only using the training set for that language; ii) multi-task where we do joint prediction of both the tasks, while only using the dataset for the corresponding language; iii) single task multilingual, where we train on the multilingual dataset for all languages but test on a single language for one task; and iv) multi-task multilingual, where we train on the multilingual dataset and test on a single language, predicting both stance and policy jointly. we use three types of transformer models for our experiments, bert @xcite , mbert @xcite , and xlm-r @xcite . for each of the setups, we run a learning rate sweep between [5e-4, 5e-5, 5e-6], taking the best model from each. we train the models with a batch size of 8, optimizing for accuracy on the development set during policy prediction and macro-averaged f1 on the development set during stance detection or joint prediction. for the single language experiments, we use the language specific version of bert, mbert, and xlm-r, whereas for the multilingual experiments, we only use the multilingual models mbert and xlm-r. the models were run for 5 epochs for the english and multilingual setups and 500 epochs for the smaller german and turkish datasets. to avoid overfitting, we use an early stopping mechanism based on the aforementioned evaluation metric with a patience of 5. we report macro-averaged f1 scores for the task of stance detection as it is a 4way classification task with labels skewed towards the delete label. for policy prediction, however, the number of labels are substantially higher, making per-label examples scarce, especially for the smaller datasets. hence, in this scenario, we report accuracy scores. baselines to ground the results, we provide a random baseline and a majority class baseline for the single task setups. the former is calculated by assigning a random label from the label set to each test instance while the latter is calculated based on distribution of the labels in the test set. single task setup for the single task setup, we use the standard fine-tuning procedure for the models. we take the [cls] representation of the combined topic and comment text and fine-tune a sequence classification head to make the predictions for stance and policy respectively. multi-task setup to make a joint prediction, we use a multi-task learning framework (mtl) with hard parameter sharing @xcite as shown in figure 2 . this is inspired by prior work which successfully employs mtl for joint classification and explanation generation @xcite . in this setting, both tasks are learnt jointly, allowing the model to leverage important label information across the tasks. in order to further enhance this sharing, we freeze the first 6 layers of the encoder for each model. moreover, to stabilise the losses and balance the learning between the two tasks, we alternate between the losses in the ratio 3:1 for stance detection and policy prediction every step. empirically, we find that this allows for the models' losses to converge for both tasks without the models overfitting. multilingual setup since the german and turkish wikipedias are smaller than the english one, the datasets reflecting their deletion discussions contain fewer instances as well. to leverage shared learning of the task across languages, in this setup, we train the models on a joint set of comments from the english, german, and turkish datasets and tested on a single language test set. while stance labels are common (with translated labels) across the three languages, policies across each language wikipedia differ, making it challenging to consolidate them to a single set of labels. to circumvent this, we manually align the turkish and german policies with the english policies. as policies can differ across languages, there is not an exact match for all policies. however, largely the policies do align across languages, see section 4.2. ## results in the following, we present the results for the experiments we conducted in order to transparently detect a stance of a users comment, i.e., detect stance alongside the policy used as a justification for the stance. ## discussion and conclusion making content moderation decisions more transparent is crucial for users to understand why decisions were ultimately made and to understand how they were discussed. prior work has shown that explanations for content removal are underutilised in content moderation. adding them leads to more user engagement, reducing the odds of future post removals @xcite . moreover, a mandate of providing users with an explanation for why their post or content was deleted is also included in upcoming regulation @xcite . thus, making content moderation more transparent and explainable is beneficial to both online platforms and end users. to aid in this moderation, we need improved nlp methods for stance detection. however, re-sources for nlp across languages are heavily skewed @xcite . this is especially the case for stance detection where multilingual resources are scarce @xcite . in an effort to address these issues, we create a multilingual dataset (consisting of three languages: english, german, and turkish) based on wikipedia deletion discussions, annotated with the stance of the discussion participant and the policy they refer to in their comment as a justification. given a comment in a content moderation discussion, we predict the stance of the moderator alongside a justification in the form of a policy. for an end-user of wikipedia, the joint prediction setup proposed here is particularly insightful as they get a high level overview of the votes for or against deletion of an article along with the policy cited as justification for the stance. for wikipedia editors themselves, such a setup can help establish good practices of citing wikipedia content policies in their comments potential candidates provided by the model. the setup can also help make a speedy decision through a bird's-eye view of the discussion with preservation of some of the nuance beyond just keep or delete labels. our results show that it is possible to learn the stance and policy prediction tasks jointly, aiding in the transparency of automatic content moderation models. for turkish, he multi-task setup outperforms the monolingual setup for stance, demonstrating that leveraging the information across the two tasks can benefit their predictions for low-resource settings. as wikipedias across languages vary in size in terms of articles and therefore discussion pages, we also experiment with a multilingual setup to support wikipedia languages with fewer data resources. to this end, we align policies across languages where possible. we find that for turkish, the smallest dataset of our experiments, this multilingual training can improve performance, especially for the policy prediction task. as there are fewer policy pages overall for turkish and german, such a setup could in the future also be used to predict policies missing in one language that exist already in another wikipedia language. community norms vary and it is important to allow for their diversity to exist while still making use of information from norms of other communities. our setup allows for such learning while still supporting the editors in making informed decisions based on the editor discussions. in the future, the task of transparent stance detection should be applied and tested on more platforms to encourage more transparent and comprehensible content moderation. as policies for content moderation differ across platforms, our model is not able to directly predict other platforms' policies in a zero-shot setting. however, the model could be fine-tuned on another platforms' policies and potentially leverage the policy-content relationship observed on wikipedia we believe that the general framework that we propose as well as the challenges faced in defining the problem as a classification task has implications beyond wikipedia, to most platforms where norms or policies are explicitly defined. we would also look to further investigate the arguments used within the comments, along with the policies mentioned, to increase the transparency and potentially model performance across the tasks. lu xiao and niraj sitaula. 2018. sentiments in wikipedia articles for deletion discussions. in transforming digital worlds -13th international conference, iconference 2018, sheffield, uk, march 25-28, 2018, proceedings, volume 10766 of lecture notes in computer science, pages 81-86. springer.