7 research outputs found
HungerGist: An Interpretable Predictive Model for Food Insecurity
The escalating food insecurity in Africa, caused by factors such as war,
climate change, and poverty, demonstrates the critical need for advanced early
warning systems. Traditional methodologies, relying on expert-curated data
encompassing climate, geography, and social disturbances, often fall short due
to data limitations, hindering comprehensive analysis and potential discovery
of new predictive factors. To address this, this paper introduces "HungerGist",
a multi-task deep learning model utilizing news texts and NLP techniques. Using
a corpus of over 53,000 news articles from nine African countries over four
years, we demonstrate that our model, trained solely on news data, outperforms
the baseline method trained on both traditional risk factors and human-curated
keywords. In addition, our method has the ability to detect critical texts that
contain interpretable signals known as "gists." Moreover, our examination of
these gists indicates that this approach has the potential to reveal latent
factors that would otherwise remain concealed in unstructured texts
A review on deep learning applications in prognostics and health management
Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain
From Posts to Pavement, or Vice Versa? The Dynamic Interplay between Online Activism and Offline Confrontations
This study examines how the relationship between social media discourse and offline confrontations in social movements, focusing on the "Black Lives Matter" (BLM) protests following George Floyd's death in 2020. While social media's role in facilitating social movements is well-documented, its relationship with offline confrontations remains understudied. To bridge this gap, we curated a dataset comprising 108,443 Facebook posts and 1,406 offline BLM protest events. Our analysis categorized online media framing into "consonance" (alignment) and "dissonance" (misalignment) with the perspectives of different involved parties. Our findings indicate a reciprocal relationship between online activism support and offline confrontational occurrences. Online support for the BLM, in particular, was associated with less property damage and fewer confrontational protests in the days that followed. Conversely, offline confrontations amplified online support for the protesters. By illuminating this dynamic, we highlight the multifaceted influence of social media on social movements. Not only does it serve as a platform for information dissemination and mobilization but also plays a pivotal role in shaping public discourse about offline confrontations
Activism via attention: interpretable spatiotemporal learning to forecast protest activities
The diffusion of new information and communication technologies—social media in particular—has played a key role in social and political activism in recent decades. In this paper, we propose a theory-motivated, spatiotemporal learning approach, ActAttn, that leverages social movement theories and a deep learning framework to examine the relationship between protest events and their social and geographical contexts as reflected in social media discussions. To do so, we introduce a novel predictive framework that incorporates a new design of attentional networks, and which effectively learns the spatiotemporal structure of features. Our approach is not only capable of forecasting the occurrence of future protests, but also provides theory-relevant interpretations—it allows for interpreting what features, from which places, have significant contributions on the protest forecasting model, as well as how they make those contributions. Our experiment results from three movement events indicate that ActAttn achieves superior forecasting performance, with interesting comparisons across the three events that provide insights into these recent movements
MimicProp: Learning to Incorporate Lexicon Knowledge into Distributed Word Representation for Social Media Analysis
Lexicon-based methods and word embeddings are the two widely used approaches for analyzing texts in social media. The choice of an approach can have a significant impact on the reliability of the text analysis. For example, lexicons provide manually curated, domain-specific attributes about a limited set of words, while word embeddings learn to encode some loose semantic interpretations for a much broader set of words. Text analysis can benefit from a representation that offers both the broad coverage of word embeddings and the domain knowledge of lexicons. This paper presents MimicProp, a new graph-mode method that learns a lexicon-aligned word embedding. Our approach improves over prior graph-based methods in terms of its interpretability (i.e., lexicon attributes can be recovered) and generalizability (i.e., new words can be learned to incorporate lexicon knowledge). It also effectively improves the performance of downstream analysis applications, such as text classification