Finding Common Patterns in Domestic Violence Stories Posted on R eddit (2025)

Abstract: Domestic violence survivors often share their experiences in online spaces, offering valuable insights into common abuse patterns. This study analyzes a dataset of personal narratives about domestic violence from Reddit, focusing on event extraction and topic modeling to uncover recurring themes. We evaluate GPT-4 and LLaMA-3.1 for extracting key sentences, finding that GPT-4 exhibits higher precision, while LLaMA-3.1 achieves better recall. Using LLM-based topic assignment, we identify dominant themes such as psychological aggression, financial abuse, and physical assault which align with previously published psychology findings. A co-occurrence and PMI analysis further reveals the interdependencies among different abuse types, emphasizing the multifaceted nature of domestic violence. Our findings provide a structured approach to analyzing survivor narratives, with implications for social support systems and policy interventions.

## introduction narratives are central to human communication, proven to foster empathy, shared beliefs, and persuasiveness. with the growth of internet use globally, individuals increasingly share personal stories online, seeking empathy and emotional support from the online community. domestic violence stories are a striking example of this trend. the abundance of domestic violence stories on the internet provides a unique opportunity for computational analysis to identify commonalities and variations in how these experiences are narrated. by examining these stories at scale, we can uncover recurring patterns in them, such as how survivors describe the progression of abuse, the typology of abuse, the role of legal interventions, or the types of support they seek. identifying common patterns in domestic violence narratives opens the door to various applica-tions, ranging from privacy protection to early intervention strategies. for instance, detecting outlier patterns could help develop systems that prevent individuals from sharing stories that might inadvertently reveal their identities. additionally, recognizing progressions in abuse-related narratives could contribute to predictive models that identify when relationships are at risk of escalating into more severe abuse. beyond these, computational insights from these stories could be applied to support systems, legal frameworks, and advocacy efforts, ultimately improving both understanding and response strategies for domestic violence cases. to enable these potential applications, we first need to distinguish common patterns from unique details within domestic violence stories. this paper focuses on learning the recurring structures in these narratives by identifying the key events that define interactions between the victim and the perpetrator. events are central to narrative structure, and understanding which events frequently co-occur allows us to detect broader storytelling patterns. we hypothesize that domestic violence stories share a high degree of similarity, particularly in the progression of events that characterize abusive relationships. leveraging recent advancements in natural language processing (nlp), we explore the ability of large language models (llms) to extract and analyze these key events. in this paper, we propose a fully llm-based method for processing stories and attributing topics to the events, with the goal of clustering and finding similar patterns. specifically, we use llama-3.1 @xcite and gpt-4 @xcite to extract those sentences from a narrative that capture interactions between the victim and the perpetrator. we use these llms to assign topics to the extracted sentences, which facilitates learning topic progressions in the stories. we analyze topic co-occurrence and topic n-grams from the stories to find similar patterns between our set of stories. we collected a large set of domestic violence stories from reddit, consisting of more than 11,100 posts which we filtered for story-like posts, using a pre-trained classifier @xcite . our dataset is available upon request. ## related work narrative is commonly defined as a sequence of events that unfolds over time @xcite . events are the fundamental building blocks of narratives, providing structure and coherence by linking actions, participants, and consequences @xcite . earlier studies in the literature took a verbbased perspective on events, primarily focusing on extracting predicate-argument triples to represent narrative progression @xcite @xcite . more recent works have employed supervised learning, transfer learning, and sequence-tosequence models for developing models that can extract events from a piece of text @xcite @xcite @xcite . @xcite presents an extensive survey of deep learning-based methods for event extraction. identifying recurring event structures allows researchers to analyze narrative evolution, uncover causal dependencies, and detect common thematic patterns across large story datasets. while event extraction focuses on explicit actions, states, and participants, topic modeling provides a higher-level view of recurring themes within narratives, and it enables researchers to model narrative schema and arcs across large datasets @xcite @xcite @xcite . as an example, @xcite used topic modeling to find clear patterns of events that occur in birth stories and used the learned topic transition probabilities to find outlier stories. @xcite proposed a point wise mutual information (pmi) based method to capture topic segmentation for holocaust testimonies. recent advancements in transformer-based language models @xcite have enhanced computational narrative understanding. @xcite examined ways in which llms could contribute to understanding core narrative features. @xcite used gpt-4 thanks to its long context window (128k tokens) to extract trajectory mappings from a set of holocaust testimonies. @xcite fine-tuned llama @xcite and used gpt-4 in few-shot and zero-shot settings for detecting causal micronarratives within a sentence. despite their abundance and importance, domestic violence narratives have not been studied extensively in the nlp community. @xcite developed classifiers using n-grams and semantic roles as features for detecting posts on reddit discussing domestic abuse. @xcite used cnn-rnn architectures to classify between narratives containing different forms of sexual harassment shared online through a forum called safecity. @xcite studies physiological responses of readers reacting to abuse survivors studies. @xcite focused on extracting common events from a small set of domestic violence stories and developed a classifier to classify between domestic violence stories and non-domestic violence stories based on a vector distance metric. in this paper, we introduce a large set of personal domestic violence stories from reddit, and use llms to extract the events from stories and identify their topics. ## dataset to collect personal stories about domestic violence, we turned to reddit, specifically the subreddit r/domesticviolence, where users share their experiences and receive support from others. this community provides information and emotional support for victims, with members offering insights based on their personal experiences rather than professional opinions. we scraped this publicly available subreddit and archived 11,176 posts spanning from 2005 to 2021 to construct our dataset. to ensure anonymity, we only keep the posts' text. an initial exploration of the dataset revealed that not all posts contain personal experiences. some posts are general discussions or rants about domestic violence and its effects, without explicitly describing eventful personal narratives. to filter out non-narrative posts, we use storyseeker @xcite , a pretrained roberta model @xcite designed for binary classification of stories vs. non-stories. applying this model to our dataset, we identified 9,872 posts as stories (see category count non-story posts 1,304 posts classified as stories 9,872 total posts collected 11,176 ## extracting events after collecting the stories from reddit, we aimed to extract events from them to enable an analysis of themes in the stories. events are fundamental building blocks of a story, yet they are not unanimously and clearly defined in the literature. most prevailing conceptions of events are based on changes in state @xcite @xcite . @xcite categorized the relationship between verbs and time into four types: activities, achievements, accomplishments, and states. sims et al. (2019) classifies activities, achievements, accomplishments, and changes of state as "events". building on this, @xcite developed a more flexible event span annotation framework that includes not only real events but also hypothetical and recurring actions. we adopt the definition from @xcite and modify it to incorporate verbal interactions, as verbal abuse is a prevalent form of domestic abuse and we observed that it frequently appears in our dataset. the definition of event is provided in the appendix section a.1. people share their personal stories with varying levels of detail; some provide extensive background on their own or their partner's lives, while others narrate in detail the sequence of events leading up to instances of domestic violence. we focus on events involving both the victim and the perpetrator because we are most interested in uncovering patterns that characterize abusive relationships. we do not assume all aspects of these stories are alike, given the numerous ways relationships start and people's diverse life backgrounds. therefore, to identify the commonalities we believe exist in domestic violence narratives, we first extract sentences that describe events or actions that directly involve both the victim and the perpetrator. we prompt llama-3.1 8b (meta-llama/llama-3.1-8b-instruct) and gpt-4 (gpt-4-turbo) with the definition of events and a description of the task. we provide three examples in the prompt to clarify the task and serve as few-shot examples. the prompt we used for this task is available in the appendix section (a.1). we set the @xmath0.0 while prompting both models. ## generating topics for sentences after extracting sentences containing events, we generated topics for those sentences in order to uncover patterns in topics across stories. ## analysis after identifying eventful sentences and generating topics for them, we then aimed to identify patterns of topics across stories. ## conclusion in this paper, we analyzed a large dataset of domestic violence stories posted on reddit. we investigate llms' ability to extract events which involve main characters of the story. our findings suggest that despite llms showing remarkable performance across various nlp tasks, they still fall short of human-level performance for extract-ing events that meet specific conditions. we used a modern llm-based topic modeling approach, topicgpt, and find it suits our task well, as is able to assign coherent and interpretable topics to sentences in the story. our proposed method, an llm based pipeline for extracting sentences and assigning topics to them, reduces each story into a structured topic sequence, facilitating narrative analysis. using monte carlo simulations, we examined the topic sequences generated by our method, and found them to contain meaningful structures which are significantly different than any random assignment of the assigned topics. the results validate that our pipeline extracts structural patterns that are highly interpretable. in future work, we will analyze the stories with a generative approach and develop techniques for identifying narratives that deviate from predominant topic progression patterns.