Distilling Implied Bias from Hate Speech for Counter Narrative Selection (2023)

Abstract: Hate speech is a critical problem in our society and social media platforms are often an amplifier for this phenomenon. Recently the use of Counter Narratives (informative and non-aggressive responses) has been proposed as a viable solution to counter hateful content that goes beyond simple detection-removal strategies. In this paper we present a novel approach along this line of research, which utilizes the implied statement (bias) expressed in the hate speech to retrieve an appropriate counter narrative. To this end, we first trained and tested several LMs that, given a hateful post, generate the underlying bias and the target group. Then, for the counter narrative selection task, we experimented with several methodologies that either use or not use the implied bias during the process. Experiments show that using the target group information allows the system to better focus on relevant content and that implied statement for selecting counter narratives is better than the corresponding standard approach that does not use it. To our knowledge, this is the first attempt to build an automatic selection tool that uses hate speech implied bias to drive Counter Narrative selection.

## introduction when visiting social media platforms (smps), it is common to stumble upon content that is hateful or discriminatory. these posts usually address derogatory cliché about a specific community. for instance, coming across the microaggression below @xcite , the reader naturally infers that this post is targeting a specific group of people-muslims. most people would additionally recognize the indirectly-stated stereotype, a socalled false narrative, i.e., "muslims are terrorists.". without prior stereotype familiarity, one would not fully understand the implied meaning emerging from the reading. "wow, don't get the muslim mad guys! we don't want to come to a blown up school tomorrow!" smps have become means to spread this kind of inappropriate content about target groups (tg). corporations owning social media platforms counter this phenomenon through various mechanisms, such as by allowing users to report hateful posts and suspicious accounts. however, manual intervention is a time consuming activity for all parties involved. developing automated hate speech (hs) detection systems has thus become a research priority. since the early stage of hs classification done by @xcite , many competitive models and datasets emerged @xcite @xcite . however, these solutions are not sufficient and can be charged with censorship as argued in @xcite . an alternative and more promising way to tackle online hatred is by responding to hs with non-aggressive textual feedback using credible evidence and factual arguments that give alternative viewpoints @xcite . this strategy, called counter narrative (cn), is believed to be effective at moderating the hate content while preserving freedom of speech @xcite @xcite @xcite . still, since manually crafting cns is a time consuming activity, recent research started to draw attention to automatic cn generation task @xcite @xcite . to build an automatic cn generation tool, it is vital for the system to understand the underlying meaning of the hs, and not to rely solely on strongly indicative keywords, such as explicit slurs @xcite . in this regard, pragmatic inference is required to understand the complete meaning of hs, usually built upon stereotype tg hs is cn migrants immigration is a curse for britain and the world. it is destroying our civilization. immigration is bad. some countries are importing a lot more people than others. our country is wealthy, i are a thriving place to live. i should not blame immigrants. lgbt+ gays are worse that nukes. they like to force their beliefs on others. they are gaslighting us! lgbt+ have an agenda. well, since you are very scared, i am sure you have some examples on when some gay tried to convince you to join their side. i would be interested in hearing that. ## datasets three datasets were used for our experiments. social bias inference corpus (sbic) contains roughly 150,000 structured annotations of posts collected from various social media sources @xcite . the corpus is annotated following a formalism (social bias frames) that aims to cover both structured pragmatic and social implications by including various categorical and textual annotations. the authors considered labels informing whether the post was offensive, intentionally offensive, lewd, implicating any group, or using in-group language. whenever tgs and iss were available, they were annotated in a free-text format, resulting in about 34,000 detailed implication examples. iss were generally annotated in the form of simple hearst-like patterns (e.g., "immigrants are 〈adj〉"; @xcite ). some posts were annotated by multiple humans, thus receiving multiple iss. sbic covers a variety of hss that target different groups, depending on gender, race, and religion. implicit hate corpus (ihc) elsherief et al. (2021) focuses on implicit hs, and covers diverse hate language, such as indirect sarcasm, intimidation, and euphemisms. the authors define a theoretically grounded framework with a fine-grained six-class taxonomy to cover different characteristics of implicit hs. in addition to the categories, the dataset includes descriptions of is and tg labels. the posts are collected mainly from twitter, focusing on us hate groups as identified by the southern poverty law center report 1 . each post got assigned one out of six hate categories, a free-text tgs and iss formatted as hearstlike patterns. multi-target conan (mt-conan) dataset contains hs-cn pairs @xcite that are collected through an human-in-the-loop data collection methodology. this dataset was obtained starting from a seed dataset of pairs written by ngo experts and then expanded iteratively using gpt-2 to generate new examples. it covers 5,003 hs/cn pairs in seven tgs: muslims, jews, lgbt+, women, disabled, migrants, and peo-ple of color (poc). ## data preparation for is generation and cn selection tasks, data filtering and pre-processing techniques were applied to the datasets we described. for implied statements, the goal of data preparation was to keep combinations of hs, tg, and is that follow specific patterns and meet specific requirements. for this reason we: (i) selected only hateful sentences that have an annotated is, (ii) select samples with a single-sentenced is that rigorously follow the pattern ⟨subject⟩ -⟨predicate⟩ -⟨object⟩. (iii) select examples with targets that can be aligned with those in mt-conan dataset. the original datasets contain 153,498 samples from ihc and sbic while after filtering and standardization we obtained a total of 30,585 samples. we call this resulting dataset implied bias statement dataset (ibsd). a detailed description of the whole procedure can be found in appendix a. for counter narratives, instead, the goal of data preparation was simply to exclude from mt-conan the examples that were using an hs from sbic, to grant mutually exclusive data for the cn selection task. this filtering procedure resulted in a total of 4,251 examples. ## counter narrative selection in this section, we present the task of finding appropriate counter-narratives for the hateful posts with the help of implied statements. using ibsd and mt-conan datasets, we select a relevant cn for a given hs by utilizing the semantic similarities between elements of the two datasets. ## results the results of the human evaluation are shown in tables 3 and 4 . sim is+base outperforms other configurations, while filter is +sim base performs poorly. the best configuration includes all three elements of a data sample, i.e., is, hs, and cn, with is weakly constraining the produced embedding. instead, the is filtering in filter is +sim base configuration acts as a hard constraint, resulting in a limited number of cns to be fed to the sim function. this configuration assumes that constraining by is narrows down the cn set to keep, and these would likely be the most relevant ones. however, human evaluation results show that keeping only the top five most similar iss in this configuration limits the cn search space too much. even if there were many iss exactly matched between ibsd and mt-conan, the filtering function would discard all the other highly similar iss and thus some possible good cns. among the samples with one of two cn options chosen by an ensemble configuration (total of 99 samples), the cn supported by an ensemble is preferred (68.7%) over the other cn that was selected by only one configuration (31.3%). the most reliable ensemble configuration was (sim base ∩ sim is+base ). as shown in ensemble configuration % sim base ∩ sim is+base 75.8 sim is+base ∩ (filter is +sim base ) 68.8 sim base ∩ (filter is +sim base ) 61.8 the annotations in the human evaluation dataset had the majority agreement of 85.6% and perfect agreement of 39.6%, which demonstrate a fair interannotator agreement with a cohen's @xmath0.4. to sum up, the results show that applying the tg filtering drastically deteriorates the cn selection performances. using the implied statement helps choosing relevant counter narratives, despite coming with some caveats. incorporating the is, while preserving the original content (hs/cn) in the input (such as sim is+base ), allows the embedding to focus more on its semantic contents. however, if the is is used as a hard filtering (such as filter is +sim base ), it could overly confines the available cn candidate space. ## conclusion in this paper we present a novel approach for selecting counter narratives to fight hate speech based on the use of the hs implied statements. first, we distill the implied statement from the hate speech through a generation task with testing several lms/decoding methods. concerning the is generation results, the finetuned bart model with the greedy decoding method yields the best bleu and rouge scores. then, to retrieve possible counter narratives for hate speech posts, we compute semantic similarity between two input texts (with varying formats including hs, cn, hate target, and implied statements), given the assumption that semantic similarity and human judgment correlate. the human evaluation results show that filtering by tg reduces the mismatch between hs and their selected cn. while the cns selected from the configuration that included is as a part of the similarity calculation was preferred, filtering by is constrains too many utilizable cn candidates. in addition, a joint selection of the same cn by two different configurations (ensemble) yielded even better results in terms of human preference. although the novel approach of incorporating is and tg show effectiveness in the cn selection task, there are several possible directions that can be explored. addressing target imbalance in the dataset and out-of-target implied statement generation for evaluating the model performances on zero-shot out of domain experiments could be explored in the next steps. additionally, more complex semantic similarity methods could be integrated into the task.