“A Tale of Two Movements’: Identifying and Comparing Perspectives in # B lack L ives M atter and # B lue L ives M atter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction (2023)

Abstract: Social media has become a major driver of social change, by facilitating the formation of online social movements. Automatically understanding the perspectives driving the movement and the voices opposing it, is a challenging task as annotated data is difficult to obtain. We propose a weakly supervised graph-based approach that explicitly models perspectives in #BackLivesMatter-related tweets. Our proposed approach utilizes a social-linguistic representation of the data. We convert the text to a graph by breaking it into structured elements and connect it with the social network of authors, then structured prediction is done over the elements for identifying perspectives. Our approach uses a small seed set of labeled examples. We experiment with large language models for generating artificial training examples, compare them to manual annotation, and find that it achieves comparable performance. We perform quantitative and qualitative analyses using a human-annotated test set. Our model outperforms multitask baselines by a large margin, successfully characterizing the perspectives supporting and opposing #BLM.

## introduction social media platforms have given a powerful voice to groups and populations demanding change and have helped spark social justice movements. the platforms provide the means for activists to share their perspectives and form an agenda, often resulting in real-world actions. such movements can also lead to the formation of reactionary movements, aiming to counter the call for change. in this paper we suggest a computational framework for analyzing such instances of public discourse, specifically looking at the interaction between #blacklivesmatter and its reaction -#bluelivesmatter. the first movement was formed in 2013 in response to the acquittal of george zimmerman @xcite police chocked the [innocent man] blacks until he died. #blacklivesmatter #demolishthepolice #bluelivesmatter trends only when a [black person] blacks is killed. #racisminamerica @xcite blm supporters are destroying our communities. #blacklivesmatter is a terrorist group. to account for the differences in perspectives between the two movements in a nuanced way, we suggest a structured representation for analyzing online content, corresponding to multiple tasks, such as capturing differences in stances, disambiguating the entities each side focuses on, as well as their role and sentiment towards them. we discuss our analysis in section 2.2. from a technical perspective, our main challenge is to model the dependency among these tasks. for example, consider the use of the entity "thugs" in figure 1 by two different authors. understanding the perspectives underlying the texts' stance towards #blacklives-matter, requires disambiguating it as either refering to police officers, or to blacklivesmatter supporters. we model such interaction among tasks using a graph-based representation, in which authors, their posts and its analysis are represented as nodes. we use a graph neural network @xcite to model the interaction among these elements, by creating graph-contextualized node representations, which alleviate the difficulty of text analysis. we discuss our graph representation in section 3.1 and its embedding in section 3.2. social movements are dynamic, as a result, using human-annotated data for training is costly. hence, we explore learning from weak supervision, initialized with artificially generated data using large language models (llms) @xcite and amplified using self-training, exploiting the graph structure to impose output consistency. we discuss self-learning in section 3.5. to evaluate our model and the quality of artificial data, we human annotate ∼ 3k tweets by both sides for all analysis tasks. we compare our graph-based approach with multitask and discrete baselines in two settings -direct supervision (train with humanannotated data) and weak supervision (train with artificial data). our results on human-annotated test set consistently demonstrate the importance of social representations for this task and the potential of artificially crafted data. we also present an aggregate analysis of the main perspectives on 400k tweets related to the george floyd protests. ## blm data collection and analysis first, we describe the data collection process for the #blm movement. then, we define the structured perspective representation used for analyzing it. on the blm protest and the counter-protests. this dataset was collected using keyword matching and spans from 2013 to 2021. however, as shown in the paper, blm-related tweets spiked mainly after the murder of george floyd in 2020. hence, in this paper, we study the tweets that were posted in a month time span following george floyd's murder. we consider original tweets written in english and discard any author from the dataset who tweeted less than 5 times in the timeframe. the dataset statistics can be found in ## weakly supervised identification of perspectives in this section, we propose a self-supervised modeling approach for a holistic identification of perspectives and stances on #blacklivesmatter. ## training and test data collection detecting perspectives in social media texts using supervised models requires costly humanannotated training data. hence, being inspired by the recent advances in generative large language models (llms) @xcite , we artificially craft training examples using llms. to evaluate the quality of the crafted training data and to evaluate our model's performance in real test data, we human annotate a subset of real data. artificial training data generation: for generating tweets containing specific perspectives, we prompt llms @xcite in a way that all of the structured elements for the perspectives are present in the generated tweets. for example, to generate tweets that are "pro-blacklm" and portray "black americans" as "positive target", we prompt llms to generate n tweets that meet 3 conditions -(1) explicitly mention one or more entities from ('black man', 'black people', 'black woman', etc.) and portray them as positive targets, (2) are in the support of the "black lives matter" movement, (3) use one or more hashtags from (#blacklivesmatter, #blm, etc.). we follow this prompt structure for each (stance, abstract entity, perspective) tuple in ## related works the discourse of the #blm movement is mostly studied in computational social science (css) using keyword-based analyses @xcite @xcite . however, it is understudied in nlp. early works studied the problem of identifying the names of civilians in news articles killed by police using em-based @xcite and deep learning approaches @xcite . recently, ziems and yang, 2021 introduced a news corpus covering 7k police killings to study entity-centric framing of victims, defined as the demographics and other status (e.g., armed/unarmed). a shared task was proposed for identifying blmcentric events from large unstructured data sources @xcite , and @xcite introduced a large #blm tweet corpus paving the way for more studies in this area. in this paper, we propose a holistic learning framework for understanding such social movements. our work is broadly related to stance detection (küçük and can, 2020; aldayel and magdy, 2021), entity-centric sentiment analysis @xcite @xcite , entity disambiguation @xcite @xcite , data augmentation @xcite , and the works that analyze similar discourses on social media @xcite and incorporate social supervision in language understanding such as sentiment analysis (yang and eisenstein, 2017), political perspective detection @xcite , fakenews detection @xcite , and political discourse analysis @xcite . detailed discussions on the css studies on the movements, stance, perspective analysis and data augmentation techniques can be found in appendix f. ## conclusion in this paper, we propose a weakly-supervised selflearned graph-based structured prediction approach for characterizing the perspectives and discourses on the #blacklivesmatter and the #bluelivesmatter movements on social media. we evaluate our model's performance in a human-annotated test set and find a significant improvement over all baselines. finally, using our model we successfully analyze and compare perspectives expressed in both of the movements.