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.

Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts discussing concerns about anticipatory failures is under-explored. If such concerns are channeled to the appropriate authorities, it can aid in the prevention and mitigation of potential infrastructural failures. In this paper, we develop an infrastructure ombudsman -- that automatically detects specific infrastructure concerns. Our work considers several recent structural failures in the US. We present a first-of-its-kind dataset of 2,662 social web instances for this novel task mined from Reddit and YouTube.

The social web presents a modern-day instrument to analyze a wide range of behavioral research questions. Of these platforms, Twitter has played a key role in social science research for more than a decade. This paper looks into an underexplored aspect - transience of Twitter datasets and makes the following three contributions. First, via a comprehensive investigation of more than 40 Twitter datasets, we identify that many of these datasets suffer from severe retrieval loss. Second, we demonstrate that the retrieval loss across labels is often imbalanced with inappropriate labels (e.g., misinformation, hate speech) suffering from more retrieval loss. Finally, we demonstrate that imbalanced retrieval loss may impact machine learning models differently than balanced retrieval loss.

We investigate how representations of Syrian refugees (2011-2021) differ across US partisan news outlets. We analyze 47,388 articles from the online US media about Syrian refugees to detail differences in reporting between left- and right-leaning media. We use various NLP techniques to understand these differences. Our polarization and question answering results indicated that left-leaning media tended to represent refugees as child victims, welcome in the US, and right-leaning media cast refugees as Islamic terrorists. We noted similar results with our sentiment and offensive speech scores over time, which detail possibly unfavorable representations of refugees in right-leaning media. A strength of our work is how the different techniques we have applied validate each other. Based on our results, we provide several recommendations. Stakeholders may utilize our findings to intervene around refugee representations, and design communications campaigns that improve the way society sees refugees and possibly aid refugee outcomes.

Query-based triggers play a crucial role in modern search systems, e.g., in deciding when to display direct answers on result pages. We address a common scenario in designing such triggers for real-world settings where positives are rare and search providers possess only a small seed set of positive examples to learn query classification models. We choose the critical domain of self-harm intent detection to demonstrate how such small seed sets can be expanded to create meaningful training data with a sizable fraction of positive examples. Our results show that with our method, substantially more positive queries can be found compared to plain random sampling. Additionally, we explored the effectiveness of traditional active learning approaches on classification performance and found that maximum uncertainty performs the best among several other techniques that we considered.

A group of agents are said to collude if they share information or make joint decisions in a manner contrary to explicit or implicit social rules that results in an unfair advantage over non-colluding agents or other interested parties. For instance, collusion manifests as sharing answers in exams, as colluding bidders in auctions, or as colluding participants (e.g., Turkers) in crowd sourcing. This paper studies the latter, where the goal of the colluding participants is to "earn" money without doing the actual work, for instance by copying product ratings of another colluding participant, adding limited noise as attempted obfuscation. Such collusion not only yields fewer independent ratings, but may also introduce strong biases in aggregate results if undetected. Our proposed unsupervised collusion detection algorithm identifies colluding groups in crowd sourcing with fairly high accuracy both in synthetic and real data, and results in significant bias reduction, such as minimizing shifts from the true mean in rating tasks and recovering the true variance among raters.