Deep learning based topic and sentiment analysis: COVID19 information seeking on social media

Bashar, Abul; Nayak, Richi; Balasubramaniam, Thirunavukarasu

doi:10.1007/s13278-022-00917-5

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…Sentiment analysis is a natural language processing approach that attempts to analyze the sentiment and emotions of people expressed in unstructured text [27][28] [29]. This technique has been widely used in social science research for analyzing the general public's sentiment towards products, services, and any social phenomenon [30][31] [32]. 2 "I homeschooled my kids for many years so I didn't have get mine vaccinated at all.…”

Section: Data Analysesmentioning

confidence: 99%

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

Anoop

2023

Preprint

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Objectives: This study employs text mining and natural language processing approaches for analyzing and unearthing public discourse and sentiment towards the recent spiking Measles outbreaks reported across the globe. Study design: A detailed qualitative study was designed using text mining and natural language processing on the user-generated comments from Reddit, a social news aggregation and discussion website. Methods: A detailed analysis was conducted using topic modeling and sentiment analysis on Reddit comments (n=87203) posted between October 1 and December 15, 2022. Topic modeling was used to leverage major themes related to the Measles health emergency and public discourse; the sentiment analysis was performed to check how the general public responded to different aspects of the outbreak. Results: Our results revealed several intriguing and helpful themes, including parental concerns, anti-vaxxer discussions, and measles symptoms from the user-generated content. The results further confirm that even though there have been administrative interventions to promote vaccinations that affirm the parents' concerns to a greater extent, the anti-vaccination or vaccine hesitancy prevalent in the general public reduces the effect of such intercessions. Conclusions: A proactive analysis of public discourse and sentiments during health emergencies and disease outbreaks is vital. This study effectively explored public perceptions and sentiments to assist health policy researchers and stakeholders in making informed and data-driven decisions.}

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Section: Data Analysesmentioning

confidence: 99%

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

Anoop

2023

Preprint

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“…The centrality measures, Betweenness Centrality, Degree Centrality, and Prevalence, are known to be effective for analysing topical keyword impact and Brand impact (Bashar et al, 2022) (Fronzetti Colladon, 2018), whereas Keyword Centrality has similar properties of well-known proximity search (Tao & Zhai, 2007) and Page Rank measure (Wha a). We conjecture that the discerning process of information seekers for deciding on relevant documents can be included in the machine learning model by capturing and representing the relative interaction importance of keywords for observing how keywords appeared in historical documents.…”

Section: Expert Informed Knowledge Matrixmentioning

confidence: 99%

An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains

Bashar

Nayak

Knapman

et al. 2023

Social Science Computer Review

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Among the pressing issues facing Australian and other First Nations peoples is the repatriation of the bodily remains of their ancestors, which are currently held in Western scientific institutions. The success of securing the return of these remains to their communities for reburial depends largely on locating information within scientific and other literature published between 1790 and 1970 documenting their theft, donation, sale, or exchange between institutions. This article reports on collaborative research by data scientists and social science researchers in the Research, Reconcile, Renew Network (RRR) to develop and apply text mining techniques to identify this vital information. We describe our work to date on developing a machine learning-based solution to automate the process of finding and semantically analysing relevant texts. Classification models, particularly deep learning-based models, are known to have low accuracy when trained with small amounts of labelled (i.e. relevant/non-relevant) documents. To improve the accuracy of our detection model, we explore the use of an Informed Neural Network (INN) model that describes documentary content using expert-informed contextual knowledge. Only a few labelled documents are used to provide specificity to the model, using conceptually related keywords identified by RRR experts in provenance research. The results confirm the value of using an INN network model for identifying relevant documents related to the investigation of the global commercial trade in Indigenous human remains. Empirical analysis suggests that this INN model can be generalized for use by other researchers in the social sciences and humanities who want to extract relevant information from large textual corpora.

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“…Analysis on users' topic preferences at the event level. Knowing topics discussed by users and how the topics are distributed can effectively identify the differential topic preferences of users in different events [47]. Hence, we conducted topic extraction on users' microblogs about the four events to analyse users' topic preferences, and the results are shown in Table 5.…”

Section: Analysis On Usersmentioning

confidence: 99%

Detecting the public’s information behaviour preferences in multiple emergency events

Zhou

2021

Journal of Information Science

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Due to the frequent occurrence of public emergency events and the extensive use of social media in recent years, the public is more and more involved in the communication of public emergencies. As important members of the public, the rapid and massive information sharing of social media users makes them play an increasingly crucial role in the emergency processing. Hence, it is necessary to analyse information behaviours of social media users in emergency events. This article mined the information behaviours of users in multiple events to reflect the public’s behaviour preferences, aiming to provide information support for emergency handling. Specifically, we collected the user-generated contents related to emergency events, and then analysed the user-generated contents from multiple dimensions to obtain the corresponding information behaviours. Finally, based on the comparative analysis of four events, the information behaviour preferences of the public during emergencies were obtained. The experimental results indicate that the public’s behaviours in emergencies are related to their own interests and economic status, and curiosity about the details of events is the consistent appeal of the public.

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Deep learning based topic and sentiment analysis: COVID19 information seeking on social media

Cited by 10 publications

References 34 publications

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

An Informed Neural Network for Discovering Historical Documentation Assisting the Repatriation of Indigenous Ancestral Human Remains

Detecting the public’s information behaviour preferences in multiple emergency events

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