Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022)

Abstract: Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, counter narratives have emerged as an effective tool employed by NGOs to respond to online hate on social media platforms. For this reason, Natural Language Generation is currently being studied as a way to automatize counter narrative writing. However, the existing resources necessary to train NLG models are limited to 2-turn interactions (a hate speech and a counter narrative as response), while in real life, interactions can consist of multiple turns. In this paper, we present a hybrid approach for dialogical data collection, which combines the intervention of human expert annotators over machine generated dialogues obtained using 19 different configurations. The result of this work is DIALOCONAN, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.

## introduction while hate towards vulnerable groups or individuals is not a new phenomenon, the upsurge of hate speech and its proliferation is relatively recent and it is enabled by the fast spread of information in online platforms. the rise in hate speech online can even provoke violent actions offline. consequently, fighting online hate speech (hs) has become a vitally important "job for everyone" foot_0 especially for the nlp researchers. the contrast to hs and haters on social media platforms is usually carried on via user suspension, content removal or shadow banning, which can be mapped to a classification task in nlp terms. however, ai and nlp can play even a more crucial role that is not limited to classification. in fact, recently, nlg models have started to be proposed as an effective tool to counter hs by providing relevant responses. in particular, the idea is to imitate the operators of non-governmental organizations (ngo) that are actually intervening in online discussions by replying to hateful content using so-called counter narratives (cn), defined by @xcite as "communicative actions aimed at refuting hate speech through thoughtful and cogent reasons, and true and fact-bound arguments". through automatically generating cns, it is possible to aid ngo operators in their dayto-day manual activities, and therefore to partially countervail the sheer amount of hateful content posted online @xcite . despite the invaluable attempts to create hs/cn datasets and systems @xcite @xcite , up to now only datasets containing 2-turn interactions have been proposed (i. e. a hate speech and a responding counter narrative), while in real scenarios, such as on social media platforms, multi-turn dialogues are the norm. in figure 1 an example of such dialogues is provided foot_2 . therefore, multi-turn dialogue datasets are necessary for training models that can better handle online hate phenomenon. still, obtaining expert-written quality data to train such models on is not trivial. to ameliorate this problem, a recently proposed approach is the use of hybrid data collection strategies where a human and a machine collaborate to build data starting from a seed dataset of expert based examples @xcite . in this paper we follow this line of research and investigate novel strategies and algorithms that are specifically designed for multi-turn dialogues collection. in particular, we test 19 different hybrid strategies obtaining a novel dataset of more than 3k dialogical interactions between two interlocutors, one acting as the hater and the other as the ngo operator, for a total of more than 16k turns. we call this dataset dialoconan (dialogical counter-narratives collection). this is the first and most comprehensive multi-target dataset that addresses expert-based counter narrative generation in fully dialogical scenarios, and it can be downloaded at the following link: https:// github.com/marcoguerini/conan . ## related work in this work, we consider four main research areas as relevant: in particular (i) available datasets for hate speech detection, (ii) available datasets for cn generation, (iii) cn generation approaches, and (iv) hybrid data collection methodologies. hate detection. many benchmarks for automatic hs detection are currently available @xcite @xcite @xcite . regarding the systems built on top of these benchmarks, we refer the readers to the surveys by @xcite ; @xcite ; fortuna and nunes (2018) for detailed reviews. other reviews include the analysis of ethical implications @xcite and of problems such as bias replication @xcite @xcite @xcite . cn data collection. since cns have been shown to be effective in reducing linguistic violence @xcite @xcite @xcite and in changing the viewpoints of bystanders @xcite , they are beginning to be collected as training data for supervised nlg models. the investigated approaches for data collection can be listed as crawling @xcite @xcite , crowdsourcing @xcite , nichesourcing @xcite and hybrid approaches @xcite . the most relevant datasets for our work are (i) @xcite in terms of quality and target diversity, even if it only includes hs/cn pairs, and (ii) @xcite that hints at the issue of multiturn dialogues. however, in the latter the cn is only the last turn of a forum-style dialogue among more than 2 interlocutors, rather than a hs/cn multi-turn dialogue between two opposing actors. ## methodology data collection can be very difficult and time consuming when high quality data from experts are necessary. given that we need to collect whole hs/cn dialogues and not just pairs, the problem is even harder. moreover, scraping ngo operators' real interactions is not a viable solution, considering that this data can be used for account "doxing". in fact, malicious users could reverse-search the text included in a dataset to identify the operators' accounts. this would undermine their work, since they usually operate undercover, and would expose them to possible attacks. therefore, we decided to resort to hybrid approaches and run 3 different data collection sessions based on the aspects of the dialogue augmentation we want to address (either the structure, in terms of turns order, or the wording of the turns). in total we tested 19 different dialogue collection strategies. all the strategies are inserted in an author-reviewer pipeline as described by @xcite , where the author is a single dialogue creation strategy at a time, and the reviewer is represented by a team of trained annotators, who are tasked with post-editing the dialogues generated by the given author strategy. ## metrics we use several metrics to assess the performance of each strategy. these metrics are aimed to assess either the efficiency of the procedure or the quality of the obtained data. hter is an efficiency metric used to measure the post-editing effort of the annotator, and it is usually employed for sentence level translations @xcite . a value above 0.4 is generally used to account for low quality outputs, where rewriting from scratch is on par with correcting it @xcite . turn deletion is the percentage of turns that are discarded by the reviewers since their quality is too low and/or they do not fit in the current dialogue structure. the more content needs to be deleted, the less efficient the procedure is. turns swap is the percentage of turns that are moved by the reviewers from the original position they were in, to another position in the final edited dialogue. usually turns of this kind have a good quality but they do not fit the current position. novelty is utilized to check the quality of a generated dialogue by measuring its lexical difference with respect to a reference set of dialogues, and it is grounded on jaccard similarity @xcite . repetition rate (rr) measures the language diversity within a corpus using the rate of non-singleton ngram types @xcite . it is used in our experiments to evaluate each strategy in terms of its ability to provide diverse and varied examples. ## session 1: dialogue structure in session 1 we started from the hs/cn pairs in pairs gold and concatenated them in order to produce dialogue candidates with different structures. ## session 2: dialogue wording in the second session we focused on strategies aiming to obtain a new wording, given a structured dialogue. in particular, we tested 6 paraphrasing approaches on dialogold and on a part of the dialogues resulting from the first session. in this session, our overall aim is to obtain novel and diverse responses to hate. therefore, we chose to paraphrase only the cns belonging to a subset of the data collected in session 1, while keeping the corresponding hs as it is. ## session 3: generation in this session we follow the overall configuration presented in @xcite , where the author is an lm fine-tuned on the dialo gold together with the dialogues resulting from session 1 foot_6 . ## session comparison & data description finally, by comparing the results of the different sessions, we can conclude that the choice of the preferable data collection strategy firstly depends on the available input data, e. g. we might not always have gold hs/cn pairs available or multiple turns dialogues. secondly, depending on the desired output, if the priority is to obtain novel content, session 2 strategies would be the least favorite. also, the concatenation of existing pairs as in session 1 is a more cautious approach than the generation of completely new dialogues through lms. thus, session 1 strategies can be preferred for a more conservative approach, whereas session 3 strategies are better suited for a more creative data collection that comes at the cost of higher human correction effort. as a last step, we performed a sanity check in which a senior ngo expert conducted a qualitative evaluation by reading a random sample of the post-edited dialogues from each session. their feedback was positive, no critical issues were raised and all the dialogues were approved both in terms of produced cns and of their overall structure/naturalness. our final dataset, dialoco-nan, includes also the dialogues collected through the various training phases and exploratory studies of the annotators. overall, we collected 3059 dialogues for a total of 16625 turns. ## conclusion in this paper we have presented a hybrid approach for dialogue data collection in the realm of hate speech countering. these dialogues have been obtained starting from two expert-based seed datasets and then combining the intervention of human annotators over machine generated dialogues. we tested 19 different strategies for generation, focusing on two crucial aspects of dialogue, i.e. structure and wording. we analysed all these strategies in terms of efficiency of the procedure and quality of the data obtained. the result of this work is dialoconan, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an ngo operator, covering 6 targets of hate.