Just Collect, Don’t Filter: Noisy Labels Do Not Improve Counterspeech Collection for Languages Without Annotated Resources (2023)

Abstract: Counterspeech on social media is rare. Consequently, it is difficult to collect naturally occurring examples, in particular for languages without annotated datasets. In this work, we study methods to increase the relevance of social media samples for counterspeech annotation when we lack annotated resources. We use the example of sourcing German data for counterspeech annotations from Twitter. We monitor tweets from German politicians and activists to collect replies. To select relevant replies we a) find replies that match German abusive keywords or b) label replies for counterspeech using a multilingual classifier fine-tuned on English data. For both approaches and a baseline setting, we annotate a random sample and use bootstrap sampling to estimate the amount of counterspeech. We find that neither the multilingual model nor the keyword approach achieve significantly higher counts of true counterspeech than the baseline. Thus, keyword lists or multi-lingual classifiers are likely not worth the added complexity beyond purposive data collection: Already without additional filtering, we gather a meaningful sample with 7,4% true counterspeech.

## introduction abusive speech is a serious problem on social media, causing harm, division, and offline violence @xcite 1 . while bans are effective, they have two drawbacks -apart from the fact that they can restrict freedom of speech @xcite -namely that deleting abusive posts would not tackle the entire problem of online abusiveness because "only the most egregious forms of hate speech [...] are generally considered unlawful" @xcite and that banning abusive speech "can miss out on how 1 content notice: the appendix includes an error analysis with examples of abusive language, in particular slurs and anti-muslim racism. figure 1 : overview of the experimental setup societies evolve through [...] disagreement" because it "can [...] be thought of as a window into deeply rooted tensions and inequalities" @xcite . on these grounds, many organisations have started calls for the civil online community to counter and marginalise hateful and abusive messages. today, online counterspeech is an established practice with country-specific organisations in many national contexts. in contrast, annotated datasets are available only for a few languages @xcite @xcite , which restricts computational analyses using natural language processing. when trying to bridge this gap by building datasets for additional languages, we are confronted with specific challenges. in particular, as counterspeech is rare on social media, it is not clear how to best collect a dataset that is relevant for annotation, i.e. which has a reasonable chance of containing actual examples of counterspeech. while prior work uses different approaches to gather data and sometimes discusses their limitations, counterspeech data collection so far has not been studied explicitly. in this work, we evaluate whether filtering based on automatically-assigned labels can help to increase the amount of counter speech in a data sample when we do not have annotated data available in the language we work with (see overview 1). the setting for this evaluation is the creation of a german counterspeech dataset. we study two approaches which assign provisional labels to tweets: a) a keyword-based approach to detect abusive speech as a precondition for counterspeech replies and b) a multilingual language model fine-tuned on an english counterspeech dataset. as a baseline, we also take a random sample from the original dataset without applying any additional filtering. all three samples are manually annotated by the first author for abusive speech, counterspeech, and neutral speech using definitions by @xcite . to derive estimates of the counter speech counts in the original datasets, we use bootstrap sampling with the annotated samples. for our evaluation we collect an initial dataset of replies to german-speaking political public figures. we use the provisional labels to filter the dataset and draw a random sample for each approach. both evaluated filter approaches represent tradeoffs between resource availability and reasonable effort motivated by the goal of increasing data relevance for annotation. that is, we want to increase the amount of true counterspeech in a sample, not necessarily develop the most accurate classifiers. for example, it is acceptable if our multilingual model has mediocre performance on english text as long as it helps to increase the german sample's amount of counter speech as part of the complete setup. we find that both the multilingual model and the keyword approach achieve higher boostrap mean counts of true counterspeech than the baseline. however, confidence intervals overlap, so it is not likely that these gains generalize beyond our samples. nevertheless, already without additional filtering, our data collection produces a meaningful sample with 7,4% true counterspeech. thus, data collection efforts should weigh the added complexity against unlikely benefits. if filter methods are to be used and the additional effort seems justified, methods need to be improved substantially over the presented approaches. ## related work as investigating the dynamics and types of counterspeech has found increasing interest, there is a variety of approaches to data collection. one line of work has investigated methods to source examples from study participants, either from experts @xcite or the crowd @xcite , also by means of post-editing the output of text generation models @xcite . while these works are focused on collecting high-quality examples for counterspeech generation, we want to collect counterspeech from social media to improve classification and analyses of naturally occurring counterspeech. work which gathers data from social media falls into two categories. the first category collects examples based on identifying counterspeech communities. examples are @xcite and @xcite who retrieve tweets of accounts known to produce counterspeech or encourage other users to do so and collect these replies. similarly, @xcite identify specific communities to find abusive speech and counterspeech in the conversations. these approaches inform our initial data collection (see section 3). the second category is based on abusive content as trigger for counterspeech. @xcite built a dataset by manually searching for youtube videos with abusive titles to collect counterspeech in the comments below. in a similar way, albanyan and blanco (2022) use abusive tweets from an existing corpus to query twitter for additional abusive content to then annotate the replies to the abuse in expectation of counterspeech. @xcite use a keyword list to find potential abusive content, while @xcite and @xcite use templates of abusive phrases, all assuming to potentially find counterspeech among replies to abusive posts. counterspeech collection from abusive content inspires one of the filter approaches (based on abusive keywords) which we evaluate (see section 4). @xcite use an approach spanning both mentioned categories with a keyword list for both abusive speech and counterspeech. although there has been significant focus on abusive speech in german @xcite @xcite @xcite , there has been limited exploration of counterspeech. as a notable exception, @xcite build a large german counterspeech dataset by using a unique period in german social media where users labeled themselves as proponents of abusive speech (i.e., extreme right-wing group) or counterspeech. however, due to this distant supervision the labels are less accurate. also, the dataset is not publicly available to protect the privacy of users in this particularly sensitive context (see also @xcite . ## data collection to collect data for our study, we chose twitter as a platform due to its accessibility (at the time of data collection). we first qualitatively explored conversations around political german-speaking twitter accounts. a key observation was that abusive speech usually does not appear in original tweets but in replies. here, users often attack the author of the original tweet, other users in the conversation or social groups which are mentioned in the initial tweet. therefore we selected 12 seed accounts which receive a lot of replies to their tweets, often containing abusive speech. these seed accounts include politicians, journalists and activists of different genders, ages and political views (see app. ## filter approaches we study two different filter techniques to make the dataset more relevant in terms of counterspeech: one approach based on german abusive keywords and another approach based on a multilingual language model fine-tuned on an english counterspeech dataset. the keyword approach is based on the idea that if we identify an abusive tweet in a conversation path, the path meets at least a necessary condition to also contain counterspeech. we use the keyword list of abusive terms by @xcite . nine words were deleted from the list after preliminary experiments showed that they mostly produce erroneous matches, retaining 11,295 keywords. when searching for matches, we ignore casing and only accept verbatim matches of complete tokens (no inflections, no partial matches). when filtering the dataset, we include paths where at least one tweet contains an abusive keyword, unless the only match is in the last tweet. full counts of most-frequent matches are in app. a.2. the second approach, based on a multilingual counterspeech classifier, is inspired by @xcite who trained a multilingual model for counterspeech type classification in english, italian and french. as there are no counterspeech detection annotations available for german but for other languages, training a multilingual model is a promising option to train a classifier. as base model we use xlm-t @xcite , a multilingual twitter language model derived from xlm-roberta @xcite . for fine-tuning, we use an english reddit dataset @xcite with a total of 5,000 comments for training, 1132 comments for validation and 713 comments for testing. although reddit data is of course different from twitter data because different platform affordances and cultures, using the dataset is a reasonable trade-off for a number of reasons: @xcite also use the counterspeech definition by @xcite which we base our evaluation on. the dataset represents a situation of language use similar to our dataset as it contains natural social media speech with replies to other users (that is, not isolated posts). crucially, the dataset is comparatively large and also contains a neutral class, so that abusive speech, counterspeech as well as neutral speech are represented in proportions which seem appropriate for our inital dataset. also, as the base language model xlm-t was pre-trained on twitter data, it should still represent our target domain twitter well after just a few epochs of fine-tuning on reddit data. in preliminary experiments we select the configuration with the highest f1 (0.45) for counterspeech on the english test set, accepting lower performance for abusive and neutral speech (overall f1 of 0.59, see further details on data, training and evaluation in app. a.1). to filter the dataset, we first use the trained model to classify all tweets in the initial dataset. then, we select all conversation paths with at least one comment classified as counterspeech. ## evaluation to evaluate the two filtering methods, we manually annotate samples for each method and compare the amount of counter speech produced by each method foot_0 . we take random samples of 200 paths from the filtered datasets. additionally, we create a random sample of 200 paths from the original unfiltered dataset as a baseline. we sample complete conversation paths to ensure in-context annotation (see section 5.1). in turn, as paths have different lengths, we accept that the evaluation samples contain different numbers of tweets. the baseline sample has 810 tweets, the keyword sample 1,816 tweets and the sample of the multilingual classifier 927 tweets. for a more meaningful comparison between the samples, we do not compare directly but resample to derive estimates of counter speech counts. we use bootstrap sampling @xcite with a sample size of 810, that is, the size of the smallest evaluation sample. we draw 1,500 bootstrap samples and compute the mean and 95% confidence intervals for the number of examples labelled as counterspeech. ## discussion our results do not support the hypothesis that filtering a social media dataset using abusive keywords or a (multilingual) counterspeech classifier leads to a higher amount of true counterspeech. one reason might be that counterspeech is hard to detect, in particular when trying to distinguish it from abusive speech. in a quantitative analysis of the multilingual model's prediction errors, we find that among the falsely classified counterspeech comments, the fraction of ground truth abusive speech comments (20.00%) is slightly higher than in the entire sample (18.50%). we made similar observations in the results of the keyword approach, where a higher fraction of tweets predicted as abusive speech were actually counterspeech (14.40% vs. 10.40% in the entire sample). this suggests that abusive speech and counterspeech may share similar textual features to some extent. indeed, in a qualitative analysis we find that both the correctly as well as the falsely classified counterspeech tweets, often contain harsh language against others, challenges of opinions or disagreements with opinions (see details in app. a.3). we consciously decided to keep the counterspeech classifier comparatively simple, training on isolated comments. however, during annotation, we found that context is crucial to distinguish abusive speech from counterspeech. thus, a multilingual model including context -preferably all preceding tweets in a conversation path -might help to increase the amount of counter speech examples. this importance of context is in line with findings by @xcite on the dataset we used for fine-tuning. on the flip side, we can see our results as encouragement to not spend disproportional effort on improving filtering methods. the direct path to better counterspeech detection, not just for filtering, is by creating relevant resources. our data collection method, derived from related work and exploratory analysis, seems to be able to produce fairly relevant samples even without additional filtering. ## conclusion we asked whether filter methods can help to increase the amount of counter speech in a data sam-ple when we do not have annotated data available for the given language. in a comparison of abusive keywords and a multilingual counterspeech classifier, both used to automatically assign labels for filtering, we do not find substantial evidence that they result in samples with more counterspeech. still, even without filtering, a purposive approach to data collection produces a meaningful sample with 7.4% true counterspeech in our setting. thus, filtering datasets does not seem necessary for counterspeech annotation. nevertheless, there is in principal a reasonable potential to improve filter methods. the proposed methods are necessarily imperfect, but better classifiers, e.g., trained on english twitter data including context tweets, and improved keyword lists could change results. also, a more extensive evaluation where multiple and larger samples are annotated might lead to different findings.