scholarly journals Evaluating the cohesion of municipalities’ discourse during the COVID-19 pandemic

2021 ◽  
Author(s):  
Victor Antonio Menuzzo ◽  
André Santanchè ◽  
Luiz Gomes-Jr

Social media has been used as a method to alert and raise awareness among the population to help fight the COVID-19 pandemic. We argue that the discourse of municipalities and their respective mayors may have an influence on the behavior of the population and thus directly impact COVID-19 outcomes. This paper analyzes the diversity and cohesion of these discourses through posts published on Facebook, evaluating (i) diversity of topics discussed, (ii) topic evolution, and (iii) deviation from a central discourse. We also combine this information with epidemiological data to assess impact in the outcomes. In particular, we present two different Latent Dirichlet allocation (LDA) models to analyze how topics are being discussed by municipalities/mayors and compare how cohesion is related to the evolution of the pandemic. Our initial analysis suggests that municipalities tend to employ a unified discourse as a response to the worsening of epidemic outcomes. The results of our study could help to inform governments of better communication strategies in this and future health crisis.

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.


2020 ◽  
Author(s):  
Junze Wang ◽  
Ying Zhou ◽  
Wei Zhang ◽  
Richard Evans ◽  
Chengyan Zhu

BACKGROUND The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. OBJECTIVE The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. METHODS We used a web crawler tool and a set of predefined search terms (<i>New Coronavirus Pneumonia</i>, <i>New Coronavirus</i>, and <i>COVID-19</i>) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. RESULTS Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. CONCLUSIONS Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.


2021 ◽  
Vol 5 (1) ◽  
pp. 123-131
Author(s):  
Ni Luh Putu Merawati Putu ◽  
Ahmad Zuli Amrullah ◽  
Ismarmiaty

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.  


2016 ◽  
Vol 105 ◽  
pp. 134-146 ◽  
Author(s):  
Peng Zhang ◽  
Hansu Gu ◽  
Mike Gartrell ◽  
Tun Lu ◽  
Dayi Yang ◽  
...  

2021 ◽  
Vol 26 (6) ◽  
pp. 464-472
Author(s):  
Bo HUANG ◽  
Jiaji JU ◽  
Huan CHEN ◽  
Yimin ZHU ◽  
Jin LIU ◽  
...  

The Product Sensitive Online Dirichlet Allocation model (PSOLDA) proposed in this paper mainly uses the sentiment polarity of topic words in the review text to improve the accuracy of topic evolution. First, we use Latent Dirichlet Allocation (LDA) to obtain the distribution of topic words in the current time window. Second, the word2vec word vector is used as auxiliary information to determine the sentiment polarity and obtain the sentiment polarity distribution of the current topic. Finally, the sentiment polarity changes of the topics in the previous and next time window are mapped to the sentiment factors, and the distribution of topic words in the next time window is controlled through them. The experimental results show that the PSOLDA model decreases the probability distribution by 0.160 1, while Online Twitter LDA only increases by 0.069 9. The topic evolution method that integrates the sentimental information of topic words proposed in this paper is better than the traditional model.


Author(s):  
Aqila Intan Prakerti ◽  
Avelyna Ferariya Claresta ◽  
Muhammad Rasyid Kafif Ibrahim ◽  
Nur Aini Rakhmawati

Abstrak: Indonesia saat ini sedang dihebohkan dengan yang namanya sekolah Daring. Dimana yang seharusnya sekolah adalah tempat untuk guru dan siswa mengajarkan ilmu dari pendidikan hingga perilaku secara tatap muka dan sekarang karena keadaan yang tidak bisa dihindari maka harus dilakukannya pembelajaran secara online yaitu dengan alat perantara. Permasalahan diambil dari banyaknya siswa sudah mempunyai alat komunikasi yaitu handphone dan berbagai media sosial yang sudah dikuasai seperti Instagram. Dengan maksud untuk menganalisa siswa khususnya di Indonesia, sikap apa yang diambil ketika siswa menggunakan Instagram ketika sedang berlangsungnya pembelajaran secara daring. Didapatkan hasil ketika melakukan Teknik crawling data untuk mendapatkan teks atau caption dari penggunaan hashtag sekolah daring yaitu 120 post dalam keadaan sudah terseleksi dari yang bukan post dari siswa. Bentuk analisa untuk pengolahan data yang sudah didapat menggunakan model Latent Dirichlet Allocation (LDA) yaitu untuk menemukan topik yang mendominasi dari hashtag yang digunakan dengan penambahan fitur Stopword untuk kata yang tidak diperlukan. Hasil akhir dari analisa tersebut terdapat 4 topik yang dominan dan dimayoritasi oleh siswa yang mendapatkan penugasan dari sekolah seperti pelajaran biologi.   Kata kunci: Instagram, Latent Dirichlet Allocation (LDA), Pembelajaran Daring,   Abstract: Indonesia is currently being shocked by the named school Online. Where the school should be a place for teachers and students to teach knowledge from education to face-to-face behavior and now because of circumstances that cannot be avoided,learning must be carried out online, namely with an intermediary tool. The problem is taken from the number of students who already have communication tools, namely mobile phones and various social media that have been mastered such as Instagram. With a view to analyzing students, especially in Indonesia, what attitudes are taken when students use Instagram when learning is taking place online. Obtained results when performing techniques crawling data to get text or captions from the use hashtags, of online school namely 120 posts in a selected state from non- posts student. The form of analysis for processing the data that has been obtained uses the model, Latent Dirichlet Allocation (LDA) which is to find the dominant topic of the hashtags used by adding the feature Stopword for unnecessary words. The final result of the analysis, there are 4 topics that are dominant and are majored by students who get assignments from schools such as biology lessons.   Keywords: E-Learning, Instagram, Latent Dirichlet Allocation (LDA).


2017 ◽  
Vol 08 (03) ◽  
pp. 854-865 ◽  
Author(s):  
Li Zhou ◽  
Joseph Plasek ◽  
Ronen Rozenblum ◽  
David Bates ◽  
Chunlei Tang

SummaryObjectives: Our goal was to identify and track the evolution of the topics discussed in free-text comments on a cancer institution’s social media page.Methods: We utilized the Latent Dirichlet Allocation model to extract ten topics from free-text comments on a cancer research institution’s Facebook™ page between January 1, 2009, and June 30, 2014. We calculated Pearson correlation coefficients between the comment categories to demonstrate topic intensity evolution.Results: A total of 4,335 comments were included in this study, from which ten topics were identified: greetings (17.3%), comments about the cancer institution (16.7%), blessings (10.9%), time (10.7%), treatment (9.3%), expressions of optimism (7.9%), tumor (7.5%), father figure (6.3%), and other family members & friends (8.2%), leaving 5.1% of comments unclassified. The comment distributions reveal an overall increasing trend during the study period. We discovered a strong positive correlation between greetings and other family members & friends (r=0.88; p<0.001), a positive correlation between blessings and the cancer institution (r=0.65; p<0.05), and a negative correlation between blessings and greetings (r=–0.70; p<0.05).Conclusions: A cancer institution’s social media platform can provide emotional support to patients and family members. Topic analysis may help institutions better identify and support the needs (emotional, instrumental, and social) of their community and influence their social media strategy.Citation: Tang C, Zhou L, Plasek J, Rozenblum R, Bates D. Comment Topic Evolution on a Cancer Institution’s Facebook Page. Appl Clin Inform 2017; 8: 854–865 https://doi.org/10.4338/ACI-2017-04-RA-0055


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