Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

Ordun, Catherine; Purushotham, Sanjay; Raff, Edward

doi:10.48550/arxiv.2005.03082

Cited by 37 publications

(38 citation statements)

References 39 publications

(58 reference statements)

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“…al. [32] did a thorough analysis of COVID-19 tweets using topic modeling and pattern matching to identify high-level trends, events with sudden spikes, distinctive topics, speed of tweeting and retweeting, and network behaviors. On a similar topic, Kwan and Lim [33], [34] used sentiment analysis, topic modeling and temporal analysis techniques on tweets to study trends and discussions about COVID-19 in various countries.…”

Section: Trend Analysis Using Publicationsmentioning

confidence: 99%

Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling

Singhal¹,

Liu²,

Blessing³

et al. 2021

Preprint

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The scientific world is changing at a rapid pace, with new technology being developed and new trends being set at an increasing frequency. This paper presents a framework for conducting scientific analyses of academic publications, which is crucial to monitor research trends and identify potential innovations. This framework adopts and combines various techniques of Natural Language Processing, such as word embedding and topic modelling. Word embedding is used to capture semantic meanings of domain-specific words. We propose two novel scientific publication embedding, i.e., PUB-G and PUB-W, which are capable of learning semantic meanings of general as well as domain-specific words in various research fields. Thereafter, topic modelling is used to identify clusters of research topics within these larger research fields. We curated a publication dataset consisting of two conferences and two journals from 1995 to 2020 from two research domains. Experimental results show that our PUB-G and PUB-W embeddings are superior in comparison to other baseline embeddings by a margin of ∼0.18-1.03 based on topic coherence.

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Section: Trend Analysis Using Publicationsmentioning

confidence: 99%

Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling

Singhal¹,

Liu²,

Blessing³

et al. 2021

Preprint

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“…Cinelli et al [1] extract topics with word embedding on a global scale, making the conclusion that social media may help to design more efficient epidemic models for social behaviour and to implement more efficient communication strategies. The LDA model is used by Medford et al [5] and Ordun et al [6] to analyse the topics in early period of the pandemic. Sharma, et al [7] use character embedding [28] and Term Frequency Inverse Document Frequency (TF-IDF) word distribution with manual inspection for topic modelling.…”

Section: Related Workmentioning

confidence: 99%

“…There is a growing body of research that links OSNs activities to COVID-19. Some existing results have already shown that OSNs conversations can be a leading indicator of COVID-19 cases [2,3], discussions on OSNs can be categorised into multiple specific topics [4,5,6,7] and OSNs may help to design more efficient pandemic models for social behaviour and to implement more responsive government communication strategies [1,8,9]. However, there are three main problems with the existing research.…”

Section: Introductionmentioning

confidence: 99%

An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

Chen

Zhong

Pang

2020

Preprint

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The outbreak of the Coronavirus disease (COVID-19) leads to an outbreak of pandemic information in major online social networks (OSNs). In the constantly changing situation, OSNs are becoming a critical conduit for people in expressing opinions and seek up-to-the-minute information. Thus, social behaviour on OSNs may become a predictor or reflection of reality. This paper aims to study the social behaviour of the public in the Greater Region (GR) and related countries based on Twitter information with machine learning and representation learning methods. We find that tweets volume only can be a predictor of outbreaks in a particular period of the pandemic. Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05, figuring out the main differences in public behaviour between GR and related countries. Finally, we conclude that tweets volume of anti-contiguous measures may affect the effeteness of the government policy.

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“…As an example, in [2], Kleinberg et al presented a ground truth dataset of emotional responses to COVID-19, and presented a framework to detect the main concerns of people in different countries on COVID-19 subject. In [3], Ordun and colleagues analyzed five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid-19 related tweets. In [4], Singh et al looked at the information and misinformation shared on Twitter.…”

Section: Introductionmentioning

confidence: 99%

Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder

Asgari-Chenaghlu,

Nikzad-Khasmakhi,

Minaee

2020

Preprint

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The novel corona-virus disease (also known as ) has led to a pandemic, impacting more than 200 countries across the globe. With its global impact, COVID-19 has become a major concern of people almost everywhere, and therefore there are a large number of tweets coming out from every corner of the world, about COVID-19 related topics. In this work, we try to analyze the tweets and detect the trending topics and major concerns of people on Twitter, which can enable us to better understand the situation, and devise better planning. More specifically we propose a model based on the universal sentence encoder to detect the main topics of Tweets in recent months. We used universal sentence encoder in order to derive the semantic representation and the similarity of tweets. We then used the sentence similarity and their embeddings, and feed them to K-means clustering algorithm to group similar tweets (in semantic sense). After that, the cluster summary is obtained using a text summarization algorithm based on deep learning, which can uncover the underlying topics of each cluster. Through experimental results, we show that our model can detect very informative topics, by processing a large number of tweets on sentence level (which can preserve the overall meaning of the tweets). Since this framework has no restriction on specific data distribution, it can be used to detect trending topics from any other social media and any other context rather than COVID-19. Experimental results show superiority of our proposed approach to other baselines, including TF-IDF, and latent Dirichlet allocation (LDA).

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Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

Cited by 37 publications

References 39 publications

Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling

Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling

An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder

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