Impact of Politically Biased Data on Hate Speech Classification
Maximilian Wich Jan Bauer Georg Groh TU Munich, TU Munich, TU Munich, Department of Informatics, Department of Informatics, Department of Informatics, Germany Germany Germany [email protected] [email protected] [email protected]
Abstract models to produce unfair or incorrect results, such as racial discrimination (Hildebrandt, 2019). This One challenge that social media platforms are phenomenon is already addressed by the research facing nowadays is hate speech. Hence, auto- community. Researchers have examined methods matic hate speech detection has been increas- ingly researched in recent years — in partic- to identify and mitigate different forms of bias, ular with the rise of deep learning. A prob- such as racial bias or annotator bias (Geva et al., lem of these models is their vulnerability to 2019; Davidson et al., 2019; Sap et al., 2019). But undesirable bias in training data. We investi- it has not been solved yet; on the contrary, more gate the impact of political bias on hate speech research is needed Vidgen et al.(2019). Secondly, classification by constructing three politically- most of the classifiers miss a certain degree of trans- biased data sets (left-wing, right-wing, polit- parency or explainability to appear trustworthy and ically neutral) and compare the performance credible. Especially in the context of hate speech of classifiers trained on them. We show that (1) political bias negatively impairs the per- detection, there is a demand for such a feature Vid- formance of hate speech classifiers and (2) an gen et al.(2019); Niemann(2019). The reason is explainable machine learning model can help the value-based nature of hate speech classification, to visualize such bias within the training data. meaning that perceiving something as hate depends The results show that political bias in training on individual and social values and social values data has an impact on hate speech classifica- are non-uniform across groups and societies. There- tion and can become a serious issue. fore, it should be transparent to the users what the 1 Introduction underlying values of a classifier are. The demand for transparency and explainability is also closely Social media platforms, such as Twitter and Face- connected to bias because it can help to uncover book, have gained more and more popularity in the bias. recent years. One reason is their promise of free In the paper, we deal with both problems. We speech, which also obviously has its drawbacks. investigate a particular form of bias — political With the rise of social media, hate speech has bias — and use an explainable AI method to vi- spread on these platforms as well (Duggan, 2017). sualize this bias. To our best knowledge, political But hate speech is not a pure online problem be- bias has not been addressed in hate speech detec- cause online hate speech can be accompanied by tion, yet. But it could be a severe issue. As an offline crime (Williams et al., 2020). example, a moderator of a social media platform Due to the enormous amounts of posts and com- uses a system that prioritizes comments based on ments produced by the billions of users every day, their hatefulness to efficiently process them. If this it is impossible to monitor these platforms manu- system had a political bias, i.e. it favors a politi- ally. Advances in machine learning (ML), however, cal orientation, it would impair the political debate show that this technology can help to detect hate on the platform. That is why we want to examine speech — currently with limited accuracy (David- this phenomenon by addressing the following two son et al., 2017; Schmidt and Wiegand, 2017). research questions: There are many challenges that must be ad- dressed when building a hate speech classifier. First RQ1 What is the effect of politically biased data of all, an undesirable bias in training data can cause sets on the performance of hate speech classi-
54 Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 54–64 Online, November 20, 2020. c 2020 Association for Computational Linguistics https://doi.org/10.18653/v1/P17 fiers? women and men. Al Kuwatly et al.(2020) were not able to confirm the gender bias with their exper- RQ2 Can explainable hate speech classification iments, but they discovered bias caused by annota- models be used to visualize a potential unde- tors’ age, educational background, and the type of sirable bias within a model? their first language. Another form that is related to We contribute to answering these two questions annotator bias is racial bias. Davidson et al.(2019) by conducting an experiment in which we con- and Sap et al.(2019) examined this phenomenon struct politically biased data sets, train classifiers and found that widely-used hate speech data sets with them, compare their performance, and use contain a racial bias penalizing the African Ameri- interpretable ML techniques to visualize the differ- can English dialect. One reason is that this dialect ences. is overrepresented in the abusive or hateful class In the paper, we use hate speech as an overar- (Davidson et al., 2019). A second reason is the ching term and define it as ”any communication insensitivity of the annotators to this dialect (Sap that disparages a person or a group on the basis of et al., 2019). To address the second problem, Sap some characteristic such as race, color, ethnicity, et al.(2019) suggested providing annotators with gender, sexual orientation, nationality, religion, or information about the dialect of a document during other characteristic” (Nockleby(2000, p.1277), as the labeling process. This can reduce racial bias. cited in Schmidt and Wiegand(2017)). Furthermore, Dixon et al.(2018) and Borkan et al. (2019) develop metrics to measure undesirable bias 2 Related Work and to mitigate it. To our best knowledge, no one, however, has investigated the impact of political 2.1 Biased Training Data and Models bias on hate speech detection so far. A challenge that hate speech detection is facing is an undesirable bias in training data (Hildebrandt, 2.2 Explainable AI 2019). In contrast to the inductive bias — the form Explainable Artificial Intelligence (XAI) is a rel- of bias required by an algorithm to learn patterns atively new field. That is why we can find only (Hildebrandt, 2019) — such a bias can impair the a limited number of research applying XAI meth- generalizability of a hate speech detection model ods in hate speech detection. Wang(2018) used (Wiegand et al., 2019; Geva et al., 2019) or can lead an XAI method from computer vision to explain to unfair models (e.g., discriminating minorities) predictions of a neural network-based hate speech (Dixon et al., 2018). classification model. The explanation was visual- There are different forms of bias. A data set, for ized by coloring the words depending on their rele- example, could have a topic bias or an author bias, vance for the classification. Svecˇ et al.(2018) built meaning that many documents are produced by a an explainable hate speech classifier for Slovak, small number of authors (Wiegand et al., 2019). which highlights the relevant part of a comment to Both forms impair the generalizability of a clas- support the moderation process. Vijayaraghavan sifier trained on such a biased data set (Wiegand et al.(2019) developed a multi-model classifica- et al., 2019). Another form of bias that has a nega- tion model for hate speech that uses social-cultural tive impact on the generalizability of classifiers is features besides text. To explain the relevance of annotator bias Geva et al.(2019). In the context of the different features, they used an attention-based hate speech detection, it is caused by the vagueness approach. (Risch et al., 2020) compared differ- of the term hate speech, aggravating reliable an- ent transparent and explainable models. All ap- notations (Ross et al., 2017). Waseem(2016), for proaches have in common that they apply local example, compared expert and amateur annotators explainability, meaning they explain not the en- — the latter ones are often used to label large data tire model (global explanation) but single instances. sets. They showed that classifiers trained on an- We do the same because there is a lack of global notations from experts perform better. Binns et al. explainability approaches for text classification. (2017) investigated whether there is a performance difference between classifiers trained on data la- 3 Methodology beled by males and females. Wojatzki et al.(2018) showed that less extreme cases of sexist speech (a Our approach for the experiment is to train hate form of hate speech) are differently perceived by speech classifiers with three different politically bi-
55 ased data sets and then to compare the performance 2014; Quan et al., 2015). But our corpus consists of these classifiers, as depicted in Figure1. To of Tweets that have a maximum length of 280 char- do so, we use an existing Twitter hate speech cor- acters. Therefore, we apply the pooling approach pus with binary labels (offensive, non-offensive), based on hashtags to generate larger documents, as extract the offensive records, and combine them proposed by Alvarez-Melis and Savesk (2016) and with three data sets each (politically left-wing, po- Mehrotra et al. (2013). litically right-wing, politically neutral) implicitly For finding an appropriate number of topics, we labeled as non-offensive. Subsequently, classifiers use the normalized pointwise mutual information are trained with these data sets and their F1 scores (NPMI) as the optimization metric to measure topic are compared. Additionally, we apply SHAP to coherence (Lau et al., 2014). The optimal number explain predictions of all three models and to com- of topics with ten keywords each (most probable pare the explanations. Our code is available on non-stop words for a topic) is calculated in a 5- GitHub1. fold cross-validation. Before generating the topic model, we remove all non-alphabetic characters, 3.1 Topic Modeling stop words, words shorter than three characters, In order to answer our research questions, we need and all words that appear less than five times in to ensure that the data sets are constructed in a the corpus during the preprocessing. Additionally, fair and comparable way. Therefore, we use an we replace user names that contain political party existing Twitter hate speech corpus with binary la- names by the party name, remove all other user bels (offensive, non-offensive) that consists of two names, and apply Porter stemming to particular 2 data sets as a starting point - GermEval Shared words (Porter et al., 1980). Only documents (cre- Task on the Identification of Offensive Language ated by hashtag pooling) that contain at least five 2018 (Wiegand et al., 2018) and GermEval Task 2, words are used for the topic modeling algorithm. 2019 shared task on the identification of offensive language (Struß et al., 2019). Combining both is 3.2 Data Collection possible because the same annotation guidelines After topic modeling of the non-offensive part from were applied. Thus, in effect, we are starting with the original data set (without augmentations), we one combined German Twitter hate speech data collect three data sets from Twitter: one from a set. In the experiment, we replace only the non- (radical) left-wing subnetwork, one from a (radical) offensive records of the original data set with po- right-wing subnetwork, and a politically neutral litically biased data for each group. To ensure that one serving as the baseline. All data was retrieved the new non-offensive records with a political bias via the Twitter API. The gathering process for these are topically comparable to the original ones, we three biased data sets is the following: use a topic model. The topic model itself is created 1. Identifying seed profiles: First of all, it is based on the original non-offensive records of the necessary to select for each subnetwork seed pro- corpus. Then, we use this topic model to obtain files that serve as the entry point to the subnetworks. the same topic distribution in the new data set with For this purpose, the following six profile cate- political bias. By doing so, we assure the new data gories are defined that have to be covered by the sets’ homogeneity and topical comparability. The selected profiles: politician, political youth organi- topic model has a second purpose besides assem- zation, young politician, extremist group, profile bling our versions of the data set. The keywords associated with extremist groups, and ideologized generated from each topic serve as the basis of the news website. In the category politician, we select data collection process for the politically neutral two profiles for each subnetwork — one female and new elements of the data set. More details can be one male. The politicians have similar positions in found in the next subsection. their parties, and their genders are balanced. For For creating the topic model, we use the Latent the category political youth organization, we took Dirichlet Allocation (LDA) algorithm (Blei et al., the official Twitter profiles from the political youth 2003). A downside of LDA, however, is that it organizations of the parties that the politicians from works well for longer documents (Cheng et al., the previous category are a member of. In the cate-
1https://github.com/mawic/ 2Frauen, Manner,¨ Linke, Rechte, Deutschland, Nazi, Jude, political-bias-hate-speech Fluchtling,¨ Grune¨
56 2 Data collection 2. Retrieving 3. Collecting additional 4. Gathering tweets Raw data 1. Identifying retweeters of profiles retweeted by from retweeters and sets seed profiles retweeters (contributors) contributors seed profiles Raw Left-wing L Raw Right-wing R
Politically Collecting Twitter stream Raw neutral based on keyword filtering N
1 Topic Modeling 3 Data Set Creation Existing data set Top keywords Label (GermEval) Filtering raw datasets according non- to original topic distribution offensive NO NO offensive OF Pool Pool Pool Filtering non- Generating topic offensive records model with keywords L R N
4 Model Training Random sampling Repeating 10 times for each
replacement rate of original Sample Sample Sample non-offensive tweets by Non-offensive ∪ ∪ ∪ politically biased ones (0%, L NO R NO N NO 33%, 66%, 100%) Offensive OF OF OF
Measuring F1 Training scores for models L R N each classifier RQ1 Extending models with interpretable framework and RQ2 analyzing selected records L R N
Figure 1: Methodological approach visualized gory young politician, we selected one profile of a ment concerning political ideology, as shown by member from the executive board of each political Conover et al.(2011a), Conover et al.(2011b), and youth organization. For the extremist group, we Shahrezaye et al.(2019). Therefore, the retweets use official classifications of official security agen- of the latest 2,000 tweets from every seed profile cies to identify one account of such a group for are retrieved - or the maximum number of available each subnetwork. Concerning the category profile tweets, if the user has not tweeted more. Unfor- associated with extremist groups, we select two tunately, the Twitter API provides only the latest accounts that associate with an extremist group ac- 100 retweeters of one tweet. But this is not a prob- cording to their statements. The statements come lem because we do not attempt to crawl the entire from the description of the Twitter account and subnetwork. We only want to have tweets that are from an interview in a newspaper. In regards to representative of each subnetwork. After collect- the ideologized news website, we again rely on the ing these retweets, we select those of their authors official classifications of a federal agency to choose (retweeters) that retweeted at least four of the seven the Twitter accounts of two news websites. We en- seed profiles. We do this because we want to avoid sure for all categories that the numbers of followers adding profiles that retweeted the seed profiles but of the corresponding Twitter accounts are compa- are not clearly part of the ideological subnetwork. rable. The seven profiles for each subnetwork are Additionally, we remove retweeters that appear in identified based on explorative research. both subnetworks to exclude left-wing accounts retweeting right-wing tweets or vice versa. More- 2. Retrieving retweeters of seed profiles: Af- over, we eliminate verified profiles. The motivation ter identifying the seven seed Twitter profiles for of deleting verified profiles is that these profiles are each political orientation as described in the pre- ran by public persons or institutions and Twitter vious paragraph, we are interested in the profiles has proved their authenticity. This transparency that retweet these seed profiles. Our assumption might influence the language the users use for this in this context is that retweeting expresses agree-
57 profile. not imply that they conform to the norms of the 3. Collecting additional profiles retweeted by medium. A tweet that complies with the norms retweeters (contributors): Step 3 aims to gather of the subnetwork, but violates the ones of the the profiles (contributors) that are also retweeted by medium could be only distributed within the sub- the retweeters of the seed profiles. Therefore, we network and does not appear in the feed of other retrieve the user timelines of the selected retweeters users. Consequently, it would not be reported and (output of step 2) to get their other retweets. From still be available. these timelines, we select those profiles that have We compose the pool data sets according to the been retweeted by at least 7.5% of the retweeters. following procedure for each politically biased data This threshold is pragmatically chosen — in abso- set: In step 1, the generated topic model assigns lute numbers 7.5% means more than 33 (left-wing) every tweet in the raw data sets a topic, which and 131 (right-wing) retweeters. The reason for is the one with the highest probability. In step setting a threshold is the same one as in step 2. 2, we select so many tweets from each topic that Besides that, profiles appearing on both sides and the following conditions are satisfied: Firstly, the verified ones are also deleted. size of the new data is about five times the size of 4. Gathering tweets from retweeters and con- the non-offensive part from the GermEval corpus. tributors: Additionally to the gathered user time- Secondly, tweets with a higher topic probability are lines from step 3, we collect the latest 2,000 tweets chosen with higher priority. Thirdly, the relative from the selected contributors (step 3), if they are topic distribution of the new data set is equal to the available. Furthermore, the profiles of selected one of the non-offensive part from the GermEval retweeters (step 2) and selected contributors (step corpus. The reason for the increased size of the 3) are monitored via the Twitter Stream API for a three new data sets (the three pool data sets) is that few weeks to collect additional tweets. we have enough data to perform several iterations The politically neutral data set is collected by in the phase Model Training in order to contribute using the Twitter Stream API. It allows us to stream to statistical validity. a real-time sample of tweets. To make sure to get relevant tweets, we filtered the stream by inputting 3.4 Model Training the keywords from the topic model we have de- In the phase Model Training, we train hate speech veloped. Since the output of the Stream API is a classifiers with the constructed data sets to compare sample of all publicly available tweets (Twitter Inc., performance differences and to measure the impact 2020), we can assume that the gathered data is not on the F1 score (RQ1). Furthermore, we make politically biased. The result of the data collection use of the ML interpretability framework SHAP to process is a set of three raw data sets - one with a explain generated predictions and visualize differ- left-wing bias, one with a right-wing bias, and one ences in the models (RQ2). politically neutral. Concerning the RQ1, the following procedure is applied. The basis is the original training corpus 3.3 Data Set Creation consisting of the union of the two GermEval data Having the topic model and the three raw data sets, sets. For each political orientation, we iteratively we can construct the pool data sets that exhibit replace the non-offensive tweets with the ones from the same topic distribution as the original non- the politically biased data sets (33%, 66%, 100%). offensive data set. They serve as pools for non- The tweets from the politically biased data sets are offensive training data that the model training sam- labeled as non-offensive. ples from, described in the next sub-section. Our For each subnetwork (left-wing, right-wing, po- assumption to label the politically biased tweets as litically neutral) and each replacement rate (33%, non-offensive is the following: Since the tweets are 66%, 100%), ten data sets are generated by sam- available within the subnetwork, they conform to pling from the non-offensive part of the original the norms of the subnetwork, meaning the tweets data set and the respective politically biased pool are no hate speech for its members. Otherwise, data set and leaving the offensive part of the orig- members of the subnetwork could have reported inal data set untouched. We then use these data these tweets, leading to a deletion in case of hate sets to train classifiers with 3-fold cross-validation. speech. The availability of a tweet, however, does This iterative approach produces multiple observa-
58 tion points, making the results more representative the SHAP values from the three different classi- — for each subnetwork and each replacement rate fiers for a selected word in a tweet indicates how we get n = 30 F1 scores. To answer RQ1, we relevant a word is for a prediction w.r.t. to a spe- statistically test the hypotheses, (a) whether the cific class (e.g., offensive, non-offensive). Figure F1 scores produced by the politically biased clas- 3a shows how these values are visualized. This sifiers are significantly different and (b) whether indication, in turn, can reveal a bias in the training the right-wing and/or left-wing classifier performs data. Therefore, we randomly select two tweets worse than the politically neutral one. If both hy- from the test set that are incorrectly classified by potheses hold, we can conclude that political bias the left-wing, respectively right-wing classifier and in training data impairs the detection of hate speech. compare their predictions to answer RQ2. The reason is that the politically neutral one is our baseline due to the missing political bias, while 4 Results the other two have a distinct bias each. Depend- 4.1 Data ing on the results, we might go one step further and might infer that one political orientation dimin- The two GermEval data sets are the basis of the ishes hate speech classification more substantially experiment. In total, they contain 15,567 Ger- than the other one. For this, we use the two-sided man tweets - 10,420 labeled as non-offensive Kolmogorov-Smirnov test (Selvamuthu and Das, and 5,147 as offensive. The data for the (radi- 2018). The null hypothesis is that the three distri- cal) left-wing subnetwork, the (radical) right-wing butions of F1 scores from three sets of classifiers one, and the neutral one was collected via the are the same. The significance level is p < 0.01. Twitter API between 29.01.2020 and 19.02.2020. If the null hypothesis is rejected, which confirms We gathered 6,494,304 tweets from timelines and (a), we will compare the average F1 scores of each 2,423,593 ones from the stream for the left-wing distribution with each other to answer (b). and right-wing subnetwork. On average, 1,026 tweets (median = 869; σ2 = 890.48) are col- The classifier consists of a non-pre-trained em- lected from 3,168 accounts. For the neutral sub- bedding layer with dimension 50, a bidirectional network, we streamed 23,754,616 tweets. After LSTM comprising 64 units, and one fully con- removing retweets, duplicates, tweets with less nected layer of the size 16. The output is a sigmoid than three tokens, and non-German tweets, we ob- function classifying tweets as offensive or not. We tain 1,007,810 tweets for the left-wing raw data used Adam optimization with an initial learning set, 1,620,492 for the right-wing raw data set, and rate of 0.001 and binary cross-entropy as a loss 1,537,793 for the neutral raw data set. 52,100 function. We applied padding to each tweet with a tweets of each raw data set are selected for the data maximal token length of 30. As a post-processing pools according to the topic model and the topic dis- step, we replaced each out-of-vocabulary token oc- tribution. The input for the 3-fold cross-validation curring in the test fold with an
59 0.86 0.86 Left-wing 0.84 Right-wing Neutral 0.84 0.82
0.80 0.82
0.78 0.80 F1-score - Overall 0.76 F1-score - Overall 0.78 0.74
0.0 0.2 0.4 0.6 0.8 1.0 Left-wing Right-wing Neutral Percentage of replacement Dataset (a) F1 scores according to replacement rate (b) F1 scores at 100% replacement rate
Figure 2: F1 scores of the three classifier subnetworks the political biases in the data seem to increase interpretable classifiers. Subsequently, we com- the performance due to the improvement of the F1 pare the explanations of the predictions from three scores. This trend, however, is misleading. The different classifiers. These explanations consist of reason for the increase is that the two classes, of- SHAP values for every token that is fed into the fensive and non-offensive, vary strongly with the classifier. They indicate the relevance of the tokens growing replacement rate, making it easier for the for the prediction. Please note: not all words of a classifiers to distinguish between the classes. More tweet are input for the classifier because some are relevant to our research question, however, are the removed during preprocessing (e.g., stop words). different steepnesses of the curves and the emerg- A simple way to visualize the SHAP values is de- ing gaps between them. These differences reveal picted in Figure 3a. The model output value is the that it is harder for a classifier trained with a po- predicted class probability of the classifier. In our litically biased data set to identify hate speech - case, it is the probability of how offensive a tweet particularly in the case of a right-wing data set. is. The words to the left shown in red (left of the While the neutral and left-wing curves are nearly box with the predicted probability) are responsible congruent and only diverge at a 100% replacement for pushing the probability towards 1 (offensive), rate, the gap between these two and the right-wing the ones to the right shown in blue (right of the curve already occurs at 33% and increases. Fig- box) towards 0 (non-offensive). The longer the ure 2b visualizes the statistical distribution of the bars above the words are, the more relevant the measured F1 scores at a 100% replacement rate as words are for the predictions. Words with a score box plots. The Kolmogorov-Smirnov test confirms lower than 0.05 are not displayed. the interpretation of the charts. The distributions Figure 3a shows the result of the three inter- of the left-wing and politically neutral data set are pretable classifiers for the following offensive not significantly different until 100% replacement tweet: @
60 neutral one 0.36. It is not surprising if we look ment rate. The gaps reflect the performance de- at the word frequencies in the three different data crease of the politically biased classifiers. The rise sets. While the word Gutmensch and related ones of the F1 scores with an increasing replacement (e.g., plural) occur 38 times in the left-wing data set rate is caused by the fact that the new non-offensive and 39 times in the neutral one, we can find it 54 tweets are less similar to the offensive ones of the times in the right-wing one. Since mostly (radical) original data set. right-wing people use the term Gutmensch to vil- The results also indicate that a right-wing bias ify political opponents (Hanisch and Jager¨ , 2011; impairs the performance more strongly than a left- Auer, 2002), we can argue that differences between wing bias. This hypothesis, however, cannot be the SHAP values can indicate a political bias of a confirmed with the experiment because we do not classifier. have enough details about the composition of the Another example of a tweet that one politically offensive tweets. It could be that right-wing hate biased classifier misclassifies is the following one speech is overrepresented in the offensive part. The (see Figure 3b): @
61 Tweet @
0.54
L Left-wing 0.09
R Right-wing 0.53
N Neutral
(a) Tweet wrongly classified by right-wing classifier
Tweet @
0.96
L Left-wing 0.10
R Right-wing 0. 35
N Neutral
(b) Tweet wrongly classified by left-wing classifier
Figure 3: SHAP values for the two selected tweets struct the baseline in the same way as the left- and speech would be filtered by all classifiers. But the right-wing data set instead of using the original data classifiers would treat comments in the gray area set as the baseline. This compensates confounding of hate speech depending on the group’s norms and factors (e.g., different time, authors). Thirdly, we values. use a sophisticated topic-modeling-based approach A limitation of this research is that we simulate to construct the data sets to ensure the new data the political bias and construct synthetic data sets sets’ topic coherence. with offensive tweets annotated by humans and non- offensive tweets that are only implicitly labeled. It 6 Conclusion would be better to have a data set annotated by We showed that political bias in training data can different political orientations to investigate the im- impair hate speech classification. Furthermore, we pact of political bias. But such an annotating pro- found an indication that the degree of impairment cess is very challenging. Another limitation is that might depend on the political orientation of bias. the GermEval data and our gathered data are from But we were not able to confirm this. Additionally, different periods. We, however, compensate this we provide a proof-of-concept of visualizing such a through our topic modeling-based data creation. bias with explainable ML models. The results can Nevertheless, political bias in hate speech data is help to build unbiased data sets or to debias them. a phenomenon that researchers should be aware of Researchers that collect hate speech to construct and that should be investigated further. All in all, new data sets, for example, should be aware of this we hope that this paper contributes helpful insights form of bias and take our findings into account in to the hate speech research and the fight against order not to favor or impair a political orientation hate speech. (e.g., politically balanced set of sources). Our ap- proach can be applied to identify bias with XAI in existing data sets or during data collection. With Acknowledgments these insights, researchers can debias a data set by, for example, adjusting the distribution of data. Another idea that is fundamentally different from This paper is based on a joined work in the context debiasing is to use these findings to build politi- of Jan Bauer’s master’s thesis (Bauer, 2020). This cally branded hate speech filters that are marked research has been partially funded by a scholar- as those. Users of a social media platform, for ex- ship from the Hanns Seidel Foundation financed ample, could choose between such filters depend- by the German Federal Ministry of Education and ing on their preferences. Of course, obvious hate Research.
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