Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media

This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.

Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

Evaluasi program sosialisasi vaksinasi covid-19 melalui media sosial

One of the evaluation parameters that can measure the effectiveness of socialization programs through social media is citizen engagement, namely public involvement in important or essential problems on social media. This evaluation was conducted to analyze the effectiveness of the Covid-19 vaccination socialization program through social media. The object of the evaluation is the netizen conversations on Twitter in the form of messages, statuses, or tweets that mention the keyword 'COVID-19 Vaccine' on social media. The effectiveness criteria of the socialization program were based on the citizen engagement index or the citizen involvement index which is examined from the netizen opinions on sentiment (positive/negative) and emotion (trust/fear) indicators. The evaluation results show that the socialization program has succeeded in increasing positive sentiment and emotions of trust. Positive sentiment was shown by netizens' opinions, which were dominated by posts that supported and accepted the vaccination program. Emotion of trust was dominated by the trust and acceptance posts. This finding, when confirmed with facts in the community, indicates a conformity. The public has supported, approved, trusted and accepted the Covid-19 vaccination.Salah satu parameter evaluasi yang dapat mengukur keefektifan program sosialisasi melalui media sosial adalah citizen engagement, yaitu keterlibatan publik terhadap suatu problematika penting atau yang dianggap penting di media sosial. Evaluasi ini dilakukan untuk menganalisis keefektifan program sosialisasi vaksinasi Covid-19 melalui media sosial. Objek evaluasi adalah percakapan warganet di Twitter baik berupa pesan, status, maupun tweet yang menyebutkan kata kunci ‘Vaksin COVID-19’ di media sosial. Kriteria keefektifan program sosialisasi mengacu kepada citizen engagement index atau indeks keterlibatan warganet yang dianalisis dari opini warganet pada indikator sentimen (positif/negative) dan emosi (trust/fear). Hasil evaluasi menunjukkan Program sosialisasi berhasil meningkatkan sentiment positif dan emosi trust. Sentimen positif ditunjukkan opini warganet yang didominasi unggahan mendukung dan menyetujui vaksinasi. Emosi trust, didominasi oleh unggahan rasa percaya dan menerima. Temuan ini apabila dikonfirmasi dengan fakta di masyarakat, mengindikasikan adanya kesesuaian. Masyarakat telah mendukung, menyetujui, percaya, dan menerima vaksinasi Covid-19.

Lexicon Based Sentiment Analysis in Indonesia Languages : A Systematic Literature Review

This systematic literature review aims to determine the trend of lexicon based sentiment analysis research in Indonesian Language in the last two years. The focus of the study is on the understanding of preprocessing used in lexicon-based sentiment analysis studies in the last two years, the lexicon used in these studies, and classification accuracy. The main question in this SLR : what techniques of lexicon based sentiment analysis will provide the highest accuracy. The most widely used preprocessing methods in previous research are tokenization, case conversion, stemming, remove punctuation, remove stop word, remove or replace emoji and emoticons, and normalization or slangword conversion. The sentiment labeling process in previous studies calculated based on the comparison of the number of negative sentiment keywords with positive sentiment keywords in one sentence. The maximum accuracy from previous study is 90%. The most widely used lexicon is NRC and Inset which is a lexicon dictionary in Indonesian. Knowledge of this can be used to propose a better model for lexicon based sentiment analysis in Indonesian Languages.

Exploring MOOCs That Promote Innovative Public Services

The promotion and dissemination of a government’s basic policy are essential to implement innovative public services to establish sustainable country development and to ensure that the benefits are shared among citizens. This study focused on the MOOCs project in Thailand, and five courses were selected for exploration. Qualitative content analysis and sentiment analysis were applied to understand how information and communications technology in government services was promoted in the courses. These methods also explained the differences in the content of each course. It turned out that the strategy of improving service quality was the most-emphasized strategy in courses with an explanation of positive sentiment. The number of users who received a positive explanation of improving service quality was estimated at 711 and rated as a satisfactory result. The result of the qualitative content analysis was assembled into groups that could reveal the government’s pleiotropic orientation in their work on basic policy. All of these groups are involved in the international criteria for a government’s digital transformation, and other activities have also been highlighted as future challenges. The possibility of using MOOCs for policy promotion and education is suggested to bridge the gap between Thailand and other countries.

Towards a Sentiment Analysis of Tweets from Online Newspapers Regarding the Coronavirus Pandemic

In the last year, both offline and online news have had the Coronavirus pandemic as their subject, especially social networking Twitter has significantly increased the news regarding Covid-19. The objectives of the project are: the analysis of news regarding the Coronavirus pandemic extracted from the Twitter profile of ANSA, a well-known Italian news agency and the analysis of sentiment and the number of likes for each news extracted The sentiment analysis has been carried out using the MAL lexicon (Morphologically Affective Lexicon), where the tweet is split into words and each paola is associated with a score. Positive (with a score greater than zero), negative (with a score less than zero) and neutral (with a score equal to zero) news were identified. As a result, it emerges that the sentiment changed day by day, so it is necessary to use sentiment indicators called indices, but only the positive sentiment index is taken into consideration as the negative one is complementary and the neutral one is almost zero. The positive index is then related to some parameters extrapolated from the Civil Protection site: number of cases, number of deaths and entry into intensive care. Furthermore, in addition to the parameters listed above, the positivity index is related to the days in which the decrees of the Prime Minister (DPCM) were signed. The last relationship analyzed is that between the average number of likes and the number of deaths. The results of the research shows that the sentiment of the news of the Ansa Agency contains 62.3% of positive news, 37.3% of negative news and only 0.3% of neutral news. Furthermore, sentiment is not influenced by the daily parameters: number of cases, number of deaths, entry into intensive care units and DPCMs. But there is a relationship between the average of like and the number of deaths. Doi: 10.28991/HIJ-2021-02-04-08 Full Text: PDF

Emotions explain differences in the diffusion of true vs. false social media rumors

False rumors (often termed “fake news”) on social media pose a significant threat to modern societies. However, potential reasons for the widespread diffusion of false rumors have been underexplored. In this work, we analyze whether sentiment words, as well as different emotional words, in social media content explain differences in the spread of true vs. false rumors. For this purpose, we collected $${\varvec{N}} =126{,}301$$ N = 126 , 301 rumor cascades from Twitter, comprising more than 4.5 million retweets that have been fact-checked for veracity. We then categorized the language in social media content to (1) sentiment (i.e., positive vs. negative) and (2) eight basic emotions (i. e., anger, anticipation, disgust, fear, joy, trust, sadness, and surprise). We find that sentiment and basic emotions explain differences in the structural properties of true vs. false rumor cascades. False rumors (as compared to true rumors) are more likely to go viral if they convey a higher proportion of terms associated with a positive sentiment. Further, false rumors are viral when embedding emotional words classified as trust, anticipation, or anger. All else being equal, false rumors conveying one standard deviation more positive sentiment have a 37.58% longer lifetime and reach 61.44% more users. Our findings offer insights into how true vs. false rumors spread and highlight the importance of managing emotions in social media content.

Tracking Public Attitudes toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-based Sentiment Analysis (Preprint)

BACKGROUND The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation poses a major barrier to achieving herd immunity. OBJECTIVE We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout. METHODS We applied a weakly-supervised aspect-based sentiment analysis (ABSA) technique on COVID-19 vaccination-related tweets in Canada. Automatically-generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiment toward “vaccination” changed over time. In addition, we analyzed the most retweeted/liked tweets by observing most frequent nouns and sentiments toward key aspects. RESULTS After training tweets using an ABSA system, we obtained 108 aspect terms (e.g., “immunity” and “pfizer”) and 6,793 opinion terms (e.g., “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying/editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for more analysis. The results showed that the top-ranked automatically-extracted aspects include “risk”, “delay”, and “hope”. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution”, “side effects”, “allergy”, “reactions” and “anti-vaxxer”, and positive sentiments related to “vaccine campaign”, “vaccine candidates”, and “immune response”. All these results indicate that the Twitter users express concerns about the safety of vaccines, but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of all the tweets, the most retweeted/liked tweets showed more positive sentiment overall, especially about vaccination itself. When looking more closely, the most retweeted/liked tweets showed an interesting dichotomy in Twitter users, i.e., the “anti-vaxxer” population who used a negative sentiment as a means to discourage vaccination and the “Covid Zero” population who used negative sentiments to encourage vaccinations while critiquing the public health response. CONCLUSIONS This study is the first to examine public sentiments toward COVID-19 vaccination on tweets over an extended period of time in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination, and get closer to the goal of ending the pandemic.

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews

Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals’ Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.

Sentiment Analysis on Covid-19 Vaccination Tweets using Naïve Bayes and LSTM

Social media is one platform where people share their opinions and views on different topics, services, or behaviors that happen around them. Since the COVID19 pandemic that started at the end of 2019, it has been a topic on which people express their sentiments. Recently, the COVID19 vaccination programs have got a lot of responses. In this paper, we have proposed two models: one based on the machine learning approach: Naive Bayes & the other based on deep learning: LSTM, whose goal is to know the sentiment of Asian region tweets towards the vaccine through sentiment analysis. The data were extracted with the help of Twitter API from March 23, 2021, till April 2, 2021. The extraction approach contains keywords with geocoding of some of the Asian countries, especially Nepal, India and Singapore. After collecting data, some preprocessing such as removing numbers, non-English & stop words, removing special characters, and hyperlinks were done. The polarity of tweets was assigned using the Text blob library. The tweets were classified into one of the three: positive, negative, or neutral. Now the data were preprocessed with the splitting of tweets into training & testing sets. Both the models were trained & tested using 10767 unique tweets. This experiment shows that a number of people in these three countries (Nepal, India and Singapore) have positive sentiment towards the vaccine and are taking the first dose of Covid19 vaccine. At last, the accuracy of the LSTM model was found to be 7% greater than that of the Naive Bayes-based model.