A Risk Communication Event Detection Model via Contrastive Learning (2020)

Abstract: This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries. The strength of our model is two-fold. First, it detects contextualized events based on topical and temporal information via contrastive learning. Second, it can be applied to multiple languages, enabling a comparison of risk communication across cultures. We present a case study and discuss future implications of the proposed model.

## introduction the novel coronavirus disease (covid-19) is affecting public health and the economy worldwide. the surge in social media usage during the pandemic led the online content to an excellent tool to examine risk communication @xcite . as more people seek and share information online, ngos and especially the who have warned of the danger of the increasing misinformation on the pandemic. a new term, infodemic, was coined to describe this phenomenon. on the other hand, the natural language processing (nlp) community's recent developments enable in-depth analysis of topical changes from online resources. latent dirichlet allocation (lda) can detect major topics from unstructured text data (e.g., extract topics in the context of a global pandemic @xcite ). advanced language models like bert can be used to learn representations (e.g., the pandemic discourse on twitter @xcite ). a language-agnostic version of bert further extends its capability to handle multiple languages @xcite . despite social media's potential to understand the risk communication pattern and infodemic during the pandemic, several challenges remain. one of them is the temporal aspect. the existing topic models which exclude temporal information cannot represent how the conversations/themes developed over time @xcite . while some works propose the refinements of such models by incorporating the time or metadata information @xcite , they still need to dissect the data into arbitrarily chosen time chunks. particularly in risk communication, where the public attention evolves quickly in a short period, the contextual information intertwining topic and time becomes a critical component @xcite . considering the time aspect also allows identifying topics that are of significant influence yet over a short span of time. addressing the limitation above, we present a time-topic cohesive model to detect contextualized events and topics over time. our model marries key ideas from contrastive learning (hereafter cl) and multilingual bert (hereafter mbert). cl is a machine learning and computer vision technique to classify similar objects by devising a triplet loss function among one anchor and two targets @xcite . by designing a triplet loss such as computing the difference of topic and time between anchor and target tweets, our model can jointly consider temporal and topical characteristics when detecting major events about the pandemic. mbert allows us to apply the model to multiple countries for comparison. the final model is applied to a collection of twitter messages gathered from three asian countries: south korea, vietnam, and iran. based on the determined events, we can understand what information (or misinformation) was mainly talked about at what stage of the pandemic in each country. unlike existing topic models, this new method also captures the temporal coherence of topics. we present a case study that shows how the risk communication on covid-19 starts with several key events initially and then expands to diverse domains in south korea. ## discussion we have extracted event trends based on the contextualized event detection model with cl in the paper. by introducing two kinds of the triplets: lda-dependent and time-dependent triplets, our model efficiently trains the embedding to gather similar events within a short period. we also present the qualitative interpretation of the trends in south korea as a possible application. as the proposed method can be executed by country, if we detect misinformation or disinformation prevalent in only one country first, we can quickly alarm other countries to deal with this issue preemptively. for future studies, we can think of utilizing other topic modeling algorithms beyond lda, like btm @xcite or docnade @xcite by considering temporal traits together, then compare the performance to the current model. some research has shown the efficacy of variational autoencoder on modeling topics @xcite in terms of both topic coherence and perplexity. besides, the framework of the current work is retrospective, and we can also try to build a monitoring/analyzing framework for detecting events in real-time. by investigating how the risk communication on covid-19 proceeds in real-time, it would be easier to react to misinformation immediately.