scholarly journals Pro/Anti-vaxxers in Brazil: a temporal analysis of COVID vaccination stance in Twitter

2021 ◽  
Author(s):  
Andre Mediote de Sousa ◽  
Karin Becker
Keyword(s):  
Federal Government ◽  
Topic Modeling ◽  
Temporal Analysis ◽  
Modeling Technique ◽  
Political Bias ◽  
Mandatory Vaccination

Collective imunization is critical to combat COVID, but a large portion of the population in many countries refuses to be vaccinated despite the availability of vaccines. We developed a temporal analysis of pro/against stances towards COVID vaccination in Brazil using Twitter. We summarized the main topics expressed by pro/anti-vaxxers using BERTopic, a dynamic topic modeling technique, and related them to events in the national scenario. The anti-vaxxers were prevalent throughout 2020, expressing concerns about mandatory vaccination with a strong political bias. The pro-vaxxer movement significantly increased by late 2020 with the begging of immunization and became prevalent in 2021. This group expresses joy and anxiety to get vaccinated and criticisms towards the Federal Government.

scholarly journals Topic Modeling Technique for Text Mining Over Biomedical Text Corpora Through Hybrid Inverse Documents Frequency and Fuzzy K-Means Clustering

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 146070-146080 ◽  
Author(s):  
Junaid Rashid ◽  
Syed Muhammad Adnan Shah ◽  
Aun Irtaza ◽  
Toqeer Mahmood ◽  
Muhammad Wasif Nisar ◽  
...  
Keyword(s):  
Text Mining ◽  
Topic Modeling ◽  
Modeling Technique ◽  
Biomedical Text ◽  
Text Corpora

A case study in text mining: Textual analysis of the Territorial Papers

2019 ◽  
Author(s):  
Johannes Ledolter ◽  
Lea VanderVelde
Keyword(s):  
Text Mining ◽  
Federal Government ◽  
Topic Modeling ◽  
The United States ◽  
State Governments ◽  
Textual Information ◽  
Modeling Methods ◽  
Frontier Settlers ◽  
Initial Statistical Analysis

Abstract The Territorial Papers of the United States are a valuable and underused resource containing almost 10,000 documents written between 1789 and 1848 about the formation of new sovereign states from US territory. These communications between the federal government and frontier settlers comprise the actual discourse of the nation’s expansion over six decades. Digitizing the Territorial Papers permits the possibility of analyzing the entire corpus globally. Text mining and topic modeling methods give us a lens on the language patterns through which new state governments and the expanding nation were formed. An initial statistical analysis of the textual information provides a visualization of content, helps discern how ideals about governance emerged, and lays the foundation for developing more sophisticated hypotheses and theoretical constructs.

Accounting for Accounting History: A topic modeling approach (1996–2015)

2017 ◽  
Vol 23 (1-2) ◽  
pp. 173-205 ◽  
Author(s):  
Paolo Ferri ◽  
Maria Lusiani ◽  
Luca Pareschi
Keyword(s):  
Topic Modeling ◽  
Modeling Technique ◽  
Accounting History ◽  
Modeling Approach ◽  
Research Areas ◽  
Theoretical Approaches ◽  
Relative Prevalence ◽  
Over Time

This article analyses all articles published in Accounting History using a topic modeling technique. Previous studies focus on the content of accounting history, but not how the field has evolved. The article complements prior assessments of the research published in Accounting History by providing measures of the relative prevalence of research areas and their evolution over time. The analysis offers insights into accounting history by refining previous categorisations, uncovering overlooked topic areas and substantiating trends, such as the demise of interest in the technical core of accounting in favour of more variegated and fragmented approaches. The findings are discussed in light of the claimed pluralisation of methodological and theoretical approaches in this field.

Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news

2016 ◽  
Vol 42 (6) ◽  
pp. 763-781 ◽  
Author(s):  
Erin Hea-Jin Kim ◽  
Yoo Kyung Jeong ◽  
Yuyoung Kim ◽  
Keun Young Kang ◽  
Min Song
Keyword(s):  
Life Span ◽  
Sentiment Analysis ◽  
News Media ◽  
Topic Modeling ◽  
Ebola Virus ◽  
Modeling Technique ◽  
Entity Extraction ◽  
Query Term ◽  
Content Coverage ◽  
N Gram

The present study investigates topic coverage and sentiment dynamics of two different media sources, Twitter and news publications, on the hot health issue of Ebola. We conduct content and sentiment analysis by: (1) applying vocabulary control to collected datasets; (2) employing the n-gram LDA topic modeling technique; (3) adopting entity extraction and entity network; and (4) introducing the concept of topic-based sentiment scores. With the query term ‘Ebola’ or ‘Ebola virus’, we collected 16,189 news articles from 1006 different publications and 7,106,297 tweets with the Twitter stream API. The experiments indicate that topic coverage of Twitter is narrower and more blurry than that of the news media. In terms of sentiment dynamics, the life span and variance of sentiment on Twitter is shorter and smaller than in the news. In addition, we observe that news articles focus more on event-related entities such as person, organization and location, whereas Twitter covers more time-oriented entities. Based on the results, we report on the characteristics of Twitter and news media as two distinct news outlets in terms of content coverage and sentiment dynamics.

Tag's Depth-Based Expert Profiling Using a Topic Modeling Technique

2020 ◽  
Vol 16 (4) ◽  
pp. 81-99
Author(s):  
Saida Kichou ◽  
Omar Boussaid ◽  
Abdelkrim Meziane
Keyword(s):  
Social Networks ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
The Other ◽  
Modeling Technique ◽  
Social Tagging ◽  
Expert Finding ◽  
Stack Overflow ◽  
Allocation Algorithm ◽  
The Right

Expert finding and expert profiling are two important tasks for organizations, researchers, and work seekers. This importance can also be seen in online communities especially with the explosion of social networks. Expert finding on one hand addresses the task of finding the right person with the appropriate knowledge or skills. Expert profiling on the other hand gives a concise and meaningful description of a candidate expert. This paper focuses on what social tagging can bring to improve expert finding and profiling. A novel expertise indicator that models and assesses an expert based on the expert's tagging activities is proposed. First, tags are used as interest indicator to build candidate's profiles; then, Latent Dirichlet Allocation algorithm (LDA) is used to construct the tags distribution over topics by exploiting the tag's semantic characteristics. Topics of interest are then filtered using tag's depth. The latter is finally used as the expertise indicator. Experiments performed on the stack overflow dataset show the accuracy of the proposed approach.

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