Overview of the G erm E val 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments (2021)

Abstract: We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. This shared task comprises three binary classification subtasks with the goal to identify: toxic comments, engaging comments, and comments that include indications of a need for fact-checking, here referred to as fact-claiming comments. Building on the two previous GermEval shared tasks on the identification of offensive language in 2018 and 2019, we extend this year’s task definition to meet the demand of moderators and community managers to also highlight comments that foster respectful communication, encourage in-depth discussions, and check facts that lines of arguments rely on. The dataset comprises 4,188 posts extracted from the Facebook page of a German political talk show of a national public television broadcaster. A theoretical framework and additional reliability tests during the data annotation process ensure particularly high data quality. The shared task had 15 participating teams submitting 31 runs for the subtask on toxic comments, 25 runs for the subtask on engaging comments, and 31 for the subtask on fact-claiming comments. The shared task website can be found at https://germeval2021toxic.github.io/SharedTask/ .

## introduction user-generated content on the web, particularly on social media, has become a regular part of our everyday life. given the heavy increase of such content within the last decade, the demand for approaches to classify online content automatically is more pressing than ever. two previous germeval shared tasks @xcite ) mark important references for research teams from both academia and industry that develop and evaluate approaches to detect offensive language in german-language online discussions. with this year's edition of germeval, we want participants to go beyond the identification of offensive comments. to this end, we extend the focus to two other classes of comments that are highly relevant to moderators and community managers on online discussion platforms: engaging comments, which should be considered to be highlighted and factclaiming comments, which should be considered as a priority for fact-checking. this shift aims to bridge the gap between the theoretical view on comment classification and the practical needs of discussion moderators. germeval is a series of shared task evaluation campaigns that focus on natural language processing for the german language and has been held since 2014. the topics of the individual shared tasks range from named entity recognition, over lexical substitution, sentiment analysis, and hierarchical classification of blurbs to the identification of offensive language. teams from both academia and industry are invited to develop and evaluate their approaches on datasets provided by the organizers. the shared tasks are run informally by self-organized groups of interested researchers and are endorsed by special interest groups within the german society for computational linguistics (gscl). the remainder of this paper is structured as follows. we describe the task in section 2 and give an overview of related work addressing the subtasks in section 3. the dataset is described in detail in section 4. in section 5, we briefly comment on the evaluation we conducted, while in section 6, we discuss the results. section 7 concludes the paper. ## task description in this section, we detail the different subtasks of the shared task. teams could participate either in all three subtasks or just in one or two of the following subtasks. every team was allowed to submit at most three runs per subtask. subtask 1: toxic comment classification. toxic, offensive, or hateful language in social media and online discussion platforms remains a widespread and particularly pressing problem. research in the field of communication science has shown that the occurrence of hate speech in online discussions decreases quality perceptions of participants and observers and may trigger stereotypical thinking, hateful commenting behavior or even withdrawal from the debate @xcite @xcite . while the automatic detection of toxic content is considered to be a promising approach in tackling this problem, it remains challenging and new approaches are constantly being developed. with this subtask we continue the series of previous germeval shared tasks on offensive language identification @xcite . ## related work detection of toxic comments. the detection of toxicity, which may also be referred to as offensive language @xcite , abusive language @xcite , hate speech @xcite , or incivility @xcite is currently one of the most active fields in natural language processing. for a recent overview of different approaches, we refer the reader to @xcite or @xcite , and to @xcite ; @xcite for a comprehensive overview of existing datasets. there has also been a high number of different shared tasks on this topic. for english, several of these shared tasks have been organized as part of the semeval shared task series @xcite @xcite . for german, there have also been two editions of germeval focusing on this task @xcite . the major difference between those two editions and this year's subtask on toxic comments is the data source. while the data by @xcite and @xcite exclusively comprise tweets, this shared task deals with facebook posts. detection of engaging comments. the task of detecting engaging comments is motivated by the idea to highlight comments that encourage and foster reasoned and civil discussions @xcite . @xcite laid groundwork by creating an annotated dataset of engaging, respectful, and informative conversations. they identified characteristics of these conversations, such as being on-topic of the discussed news article and persuasive but not sarcastic or mean. the authors used these characteristics in their follow-up work to automatically identify these conversations @xcite . @xcite introduce another publicly available dataset and use editor picks of comments posted on the website of the new york times as examples of constructive comments. examples of non-constructive com-ments comprise a subset of comments from nonconstructive threads in the dataset by @xcite . while risch and krestel (2020) applied deep learning methods to identify engaging comments automatically, there has been no related work on transformer-based models for this task. ## data & resources we manually annotated a dataset of more than 4,000 facebook user comments, which is drawn from the facebook page of a german political talk show of a national public television broadcaster. the user comments usually revolve around the political topic discussed in a particular edition of the show and contain feedback to political standpoints, the performance of talk show guests and the tv format as a whole. the training dataset contains more than 3,000 comments that were posted in the time span from january to july 2019. to constitute a realistic use case, the test dataset includes comments on editions of the show that were aired after the period of the training dataset. it includes about 1,000 comments that were posted in the time span from september to december 2020. we deliberately decided against producing our training and test data via random sampling to avoid similar word distributions in both data sets. further, since different people post comments to different editions of the talk show, it is unlikely that our dataset is dominated by the same person posting comments of a particular category (e.g. toxic comments) to any topic: our training data contain user comments of 157 especially active users debating in 141 discussion threads. therefore, we consider a topic bias and person bias @xcite unlikely. the dataset is released in anonymized form, which means that all user information and comment ids have been removed. for annotating our dataset, we made use of a theory-based annotation scheme, which is designed to identify fine-grained forms of toxic and engaging commentary behavior as well as factclaiming in online discussions @xcite ). an overview of the resulting fine-grained subcategories used in the annotation can be found in the dataset we release contains 4,188 facebook comments (training @xmath0,244, test @xmath1, which were labeled by trained annotators. high annotation quality was ensured by intensive annotator training as well as intercoder reliability testing using krippendorff's alpha. 1 apart from the discussion topic and the user id of a comment, the annotators had no access to further context information. however, it must be noted, that during their annotation, the annotators gained a certain insight into the course of the discussion, which allowed them to interpret the correct meaning of ambiguous statements. in the following, we provide a list of the finegrained communication features that constitute each of the three main categories, i.e., toxic, engaging and fact-claiming comments. annotators assigned a particular main category if they identified at least one underlying communication feature. please note, that a comment can be assigned to more than one main category at the same time. figure 1 shows examples for all three classes. toxic comments. toxic comments comprise uncivil forms of communication that can violate the rules of polite behavior, such as insulting participants of a discussion, using vulgar or sarcastic language or implied volume via capital letters. additionally, incivility can be characterized as a violation of democratic discourse values, e.g. by verbally attacking basic democratic principles or making it difficult for others to participate @xcite . it includes discrimination or discreditation of participants as well as threats of violence or the accusation of lying. engaging comments. engaging comments include behavior that is in line with deliberative principles, namely rationality, reciprocity, and mutual respect @xcite . the first category covers communication features, such as justification, solution proposals, or the sharing of personal experiences. the second category covers empathy with regard to other users' standpoints. the third category is present when the comment is in line with rules of polite interaction or includes the expression of mutual respect. fact-claiming comments. all comments that contain any assertion of facts are considered as fact-claiming comments. in addition, the provision of evidence by external sources that have been cited fall into the class of fact-claiming comment. figure 1 shows example comments of each class. ## evaluation following in the footsteps of the germeval 2019 shared on hierarchical classification of blurbs @xcite and the germeval 2020 shared task on the classification and regression of "na, welchem tech riesen hat er seine eier verkauft..?" toxic "ich macht mich wütend, dass niemand den schülerinnen gehör schenkt" not toxic (a) subtask 1: identification of toxic comments. "wie wär's mit einer kostenteilung. schließlich haben beide parteien (verkäufer und käufer) etwas von der tätigkeit des maklers. gilt gleichermassen für vermietungen. die kosten werden so oder so weiterverrechnet, eine kostenreduktion ist somit nicht zu erwarten." engaging "die aktuelle situation zeigt vor allem eines: viele kinder mussten erkennen, dass ihre mütter bestenfalls das niveau grundschule, klasse 3 haben." not engaging (b) subtask 2: identification of engaging comments. "kinder werden nicht nur seltener krank, sie infizieren sich wohl auch seltener mit dem coronavirus als ihre eltern -das ist laut ministerpräsident winfried kretschmann (grüne) das zwischenergebnis einer untersuchung der unikliniken heidelberg, freiburg und tübingen." fact-claiming "hmm...das kann ich jetzt nich nachvollziehen..." not fact-claiming (c) subtask 3: identification of fact-claiming comments. cognitive and motivational style @xcite , we use the platform codalab for evaluation. 2 the evaluation uses precision, recall, and macroaverage f1-score as metrics. macro-average f1scores give equal importance to each class, which is suited because classes in our dataset are not uniformly distributed but are equally important to identify. it is calculated as the harmonic mean of the arithmetic means of class-wise precision and recall: with p i and r i referring to precision and recall of class i out of n classes. we rank systems by their macro-average f1-score and do not consider accuracy in this shared task, since there is an imbalanced class distribution in each subtask. accuracy typically rewards correct classification of the majority class. an evaluation tool computing all of the above mentioned evaluation measures is available on the website of the shared task. ## results a high-level summary of the results by the participants in the different subtasks is given in in comparison to subtask 1, the results of subtasks 2 and 3 are more tightly clustered suggesting that the methods pursued by the different participants are similarly effective. overall, the best f1-scores reached in the different subtasks range from 69.98 (subtask 2) to 76.26 (subtask 3). these absolute numbers suggest that all three tasks are difficult and that there is still room for improvement. ## conclusion in this paper, we described the germeval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. for each of the three classes of comments, there was an individual subtask that defined a binary classification problems. as part of this shared task, we introduced a hand-annotated dataset of 4,188 facebook-posts. the results for all three subtasks show that stateof-the-art classification approaches perform well and achieve macro-average f1-scores between 70% and 76%. however, all of them should be considered far from solved. in terms of methods, we cannot determine a clear winner. all participants employed some form of transformer-based neural network. due to the complexity of that method, there is a large number of degrees of freedom, such as hyperparameters, which need to be carefully set. they still seem to have a significant impact upon the resulting overall classification performance.