# Community Needs and Assets: A Computational Analysis of Community Conversations

arXiv: https://arxiv.org/abs/2403.13272 · HTML: https://mlsanity.com/paper/2403.13272/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 74 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 27 of 27)

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- [A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework](https://mlsanity.com/paper/2203.11856/) (2022)
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- [Social Media Reveals Urban-Rural Differences in Stress across China](https://mlsanity.com/paper/2110.15726/) (2021)
- [Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response](https://mlsanity.com/paper/2402.13528/) (2024)
- [Lady and the Tramp Nextdoor: Online Manifestations of Economic Inequalities in the Nextdoor Social Network](https://mlsanity.com/paper/2304.05232/) (2023)
- [The Geography of Facebook Groups in the United States](https://mlsanity.com/paper/2201.12513/) (2022)
- [Comfort Foods and Community Connectedness: Investigating Diet Change during COVID-19 Using YouTube Videos on Twitter](https://mlsanity.com/paper/2305.11398/) (2023)

## Abstract (arXiv)

A community needs assessment is a tool used by non-profits and government agencies to quantify the strengths and issues of a community, allowing them to allocate their resources better. Such approaches are transitioning towards leveraging social media conversations to analyze the needs of communities and the assets already present within them. However, manual analysis of exponentially increasing social media conversations is challenging. There is a gap in the present literature in computationally analyzing how community members discuss the strengths and needs of the community. To address this gap, we introduce the task of identifying, extracting, and categorizing community needs and assets from conversational data using sophisticated natural language processing methods. To facilitate this task, we introduce the first dataset about community needs and assets consisting of 3,511 conversations from Reddit, annotated using crowdsourced workers. Using this dataset, we evaluate an utterance-level classification model compared to sentiment classification and a popular large language model (in a zero-shot setting), where we find that our model outperforms both baselines at an F1 score of 94% compared to 49% and 61% respectively. Furthermore, we observe through our study that conversations about needs have negative sentiments and emotions, while conversations about assets focus on location and entities. The dataset is available at https://github.com/towhidabsar/CommunityNeeds.