Social media analysis of Twitter tweets related to ASD in 2019-2020, with particular attention to Covid-19: topic modelling and sentiment analysis (Preprint)

2021 ◽  
Author(s):  
Luca Corti ◽  
Michele Zanetti ◽  
Giovanni Tricella ◽  
Maurizio Bonati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Factorization Method ◽  
Autism Spectrum ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Related Information ◽  
Data Collection Process ◽  
Use Of Social Media ◽  
Negative Sentiment

BACKGROUND Social media contains an overabundance of health information relating to people living with different type of diseases. Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with lifelong impacts and reported trends have revealed a considerable increase in prevalence and incidence. Research had shown that the ASD community provides significant support to its members through Twitter, providing information about their values and perceptions through their use of words and emotional stance. OBJECTIVE Our purpose was to analyze the messages posted on Twitter platform regarding ASD and analyze the topics covered within the tweets, in order to understand the attitude of the various people interested in the topic. In particular, we focused on the discussion of ASD and Covid-19. METHODS The data collection process was based on the search for tweets through hashtags and keywords. After bots screening, the NMF (Non-Negative Matrix Factorization) method was used for topic modeling because it produces more coherent topics compared to other solutions. Sentiment scores were calculated using AFiNN for each tweet to represent its negative to positive emotion. RESULTS From the 2.458.929 tweets produced in 2020, 691.582 users were extracted (188 bots which generated 59.104 tweets), while from the 2.393.236 total tweets from 2019, the number of identified users was 684.032 (230 bots which generated 50.057 tweets). The number of tweets and the topics covered are very similar between 2019 and 2020. The total number of Covid-asd tweets is only a small part of the total dataset. Often, the negative sentiment identified in the sentiment analysis referred to anger towards Covid-19 and its management, while the positive sentiment reflected the necessity to provide constant support to people with ASD. CONCLUSIONS Social media contributes to a great discussion on topics related to autism, especially with regards to focus on family, community, and therapies. The Covid-19 pandemic increased the use of social media, especially during the lockdown period. It is important to help develop and distribute appropriate, evidence-based ASD-related information.

scholarly journals HPV Vaccination: Polish-Language Facebook Discourse Analysis

2022 ◽  
Vol 19 (2) ◽  
pp. 914
Author(s):  
Karolina Sobeczek ◽  
Mariusz Gujski ◽  
Filip Raciborski
Keyword(s):  
Social Media ◽  
Human Papillomavirus ◽  
Qualitative Analysis ◽  
Hpv Vaccination ◽  
Media Analysis ◽  
Social Media Analysis ◽  
Related Information ◽  
Social Media Platforms ◽  
Polish Language ◽  
Negative Sentiment

Social media platforms are widely used for spreading vaccine-related information. The objectives of this paper are to characterize Polish-language human papillomavirus (HPV) vaccination discourse on Facebook and to trace the possible influence of the COVID-19 pandemic on changes in the HPV vaccination debate. A quantitative and qualitative analysis was carried out based on data collected with a tool for internet monitoring and social media analysis. We found that the discourse about HPV vaccination bearing negative sentiment is centralized. There are leaders whose posts generate the bulk of anti-vaccine traffic and who possess relatively greater capability to influence recipients’ opinions. At the beginning of the COVID-19 pandemic vaccination debate intensified, but there is no unequivocal evidence to suggest that interest in the HPV vaccination topic changed.

Predicting the French Stock Market Using Social Media Analysis

2015 ◽  
Vol 7 (2) ◽  
pp. 70-84
Author(s):  
Vincent Martin ◽  
Emmanuel Bruno ◽  
Elisabeth Murisasco
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Percentage Error ◽  
Social Media Analysis ◽  
Single Output ◽  
The Neural Network ◽  
Simple Neural Network

In this article, the authors try to predict the next-day CAC40 index. They apply the idea of Johan Bollen et al. from (Bollen, Mao, & Zeng, 2011) on the French stock market and they conduct their experiment using French tweets. Two analyses are applied on tweets: sentiment analysis and subjectivity analysis. Results of these analyses are then used to train a simple neural network. The input features are the sentiment, the subjectivity and the CAC40 closing value at day-1 and day-0. The single output value is the predicted CAC40 closing value at day+1. The authors propose an architecture using the JEE framework resulting in a better scalability and an easier industrialization. The main experiments are conducted over 5 months of data. The authors train their neural network on the first of the data and they test predictions on the remaining quarter. Their best run gives a direction accuracy of 80% and a mean absolute percentage error (MAPE) of 2.97%. In another experiment, the authors retrain the neural network each day which decreases the MAPE to 1.14%.

scholarly journals Creating Brand Image Profile by Social Media Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 8-15
Author(s):  
İbrahim Sabuncu ◽  
Berivan Edeş ◽  
Doruk Sıtkıbütün ◽  
İlayda Girgin ◽  
Kadir Zehir
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Brand Image ◽  
Media Analysis ◽  
Alternative Methods ◽  
Social Media Analysis ◽  
Focus Group Study ◽  
Complementary Method ◽  
Mining Tool

The purpose of creating a brand image profile is to measure the brand perception of consumers considering brand attributes. Thus, marketing decisions can be made based on the brand's strengths and weaknesses by determining them. The brand image profile is traditionally created using the attitude scales and surveys. However, alternative methods are needed since the questionnaires' responses are careless, the number of participants is relatively low and the cost per participant is high. In this study, as an alternative method, creating a brand image profile by analyzing social media data with artificial intelligence was made for the iPhone product. Firstly, the focus group study determined the attributes related to the last version of the iPhone. Then, between December 17th, 2019 and March 23rd, 2020, 87.227 tweets that include these attributes in English were collected from the Twitter social media platform through the RapidMiner data mining tool. Sentiment analysis was performed on collected tweets by the MeaningCloud text mining tool. In this analysis, positive and negative emotions were tried to be detected through artificial intelligence algorithms. Net Brand Reputation Score (NBR) was calculated using the positive and negative tweets amount for each attribute separately. Brand image profile was created by skew analysis using NBR values. As a result, it is thought that social media analysis can be a complementary method that can be used with traditional methods in creating a brand image profile. So, it is seen as an inevitable method to use in further studies to make sentiment analysis by processing raw data received from the Social Media platforms through artificial intelligence algorithms to transform the product label or the perspectives of an event into meaningful information.

scholarly journals Sentiment Analysis of Movies on Social Media using R Studio

2019 ◽  
Vol 8 (6) ◽  
pp. 2171-2175
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Literature Study ◽  
Social Media Analysis ◽  
Customer Feedback ◽  
Twitter Data ◽  
User Data ◽  
Market Trends ◽  
Twitter Data Analysis

This paper presents sentiment analysis of twitter data on movies using R-studio. Twitter is one of the largest social media that shares user opinion about a thing or event that happens all around the world. Recently social media analysis gained importance in digital marketing. User tweets about a product or event, person, movie, etc., are analyzed to know market trends and customer feedback. In this paper, first we have performed literature study on various methods used in twitter data analysis. Second, we have discussed about the steps involved in accessing twitter data. Finally, we have performed sentiment analysis on tweeter data for the movies titled kabali, Bharath Ane Nenu Mersal, and Dangal. User data for the movies are classified into positive, neutral and negative based on DBM and SVM. Sentiment scores are used as evaluation metrics. Results shows DBM is effective in classifying sentiments and produced better sentiment scores compared to SVM. Results are helpful in identifying popularity of the movies and audience feedback about the movies.

scholarly journals Marketing #WeTheNorth: Analyzing Fandom, Nationalism, and Brand Loyalty of the Toronto Raptors Basketball Team

2021 ◽  
Author(s):  
Sasha C. Ricciuti
Keyword(s):  
Social Media ◽  
Brand Loyalty ◽  
Research Question ◽  
Media Analysis ◽  
Social Media Analysis ◽  
The Face ◽  
Twitter Account ◽  
Research Questions ◽  
Negative Sentiment ◽  
Fan Engagement

Established as a successful marketing slogan during the 2014 NBA playoffs, the #WeTheNorth campaign became the face of branding for the Toronto Raptors franchise that enhanced brand loyalty and unified Canadian basketball fans. The following Major Research Project (MRP) explores two different research questions surrounding a social media analysis of the Toronto Raptors #WeTheNorth campaign. The first research question examines the Raptors’ fan perspective, and focuses on the connotative messages that are incorporated into the #WeTheNorth campaign to broaden the team’s message and re-vamp the team’s national identity. The second research question examines the organization’s perspective and focuses on how the Raptors brand utilizes sports nationalism in their social media efforts to support fan engagement. This paper also reinforces research from previous academic findings that include: nationalism, community, collective fandom, social media, semiotics, and branding. Using an analytics tool named Sysomos, a content analysis of the Raptors’ official Twitter account was conducted to gather primary research. One Hundred Tweets were gathered per research question, and then coded to provide insight regarding the #WeTheNorth campaign from the 2018/19 NBA regular season. Findings for the first research question reinforce national fandom, and the support fan unification via the use of the #WeTheNorth hashtag. In addition, over 35% of Tweets from fans included positive sentiment, compared to the 17% that had negative sentiment. Findings for the second research question focus on branding, semiotics, and fan engagement levels that the Toronto Raptors social media team tries to enforce. Results proved that over 60% of Tweets included some form of request for fan participation, with 17% of Tweets containing positive Tweet sentiment. Overall, as long as the #WeTheNorth campaign remains the Raptors’ primary marketing slogan the campaign should continue to reinforce national fandom and support positive online fan engagement.

scholarly journals A Social Media Analysis of the Gendered Representations of Female and Male Athletes During the 2018 Commonwealth Games

2020 ◽  
Vol 13 (4) ◽  
pp. 670-695
Author(s):  
Elaine Chiao Ling Yang ◽  
Michelle Hayes ◽  
Jinyan Chen ◽  
Caroline Riot ◽  
Catheryn Khoo-Lattimore
Keyword(s):  
Social Media ◽  
Female Athletes ◽  
Media Analysis ◽  
Sporting Events ◽  
Social Media Analysis ◽  
Male Athletes ◽  
Sport Culture ◽  
Women And Girls ◽  
Use Of Social Media ◽  
Commonwealth Games

Contemporary sport culture is characterized as highly masculinized, where female athletes are continually marginalized in traditional media. Despite evidence suggesting that media representation of athletes has a meaningful impact on social outcomes and participation rates of women and girls, little is known about gendered representations of athletes on social media and in the context of mega-sporting events. This paper examines the gendered representations of athletes on Twitter during the 2018 Commonwealth Games using framing theory. A total of 133,338 tweets were analyzed using sentiment and word-frequency analyses. Results indicate gender differences concerning athlete representation on Twitter, albeit marginal. In particular, the findings reveal that seemingly neutral words (e.g., “dedicated,” “talented,” and “hard working”) could carry gendered connotations. Recommendations are provided to guide stakeholders to advance a more inclusive sport culture through the strategic use of social media during mega-sporting events.

Aspect-based Sentiment Analysis using Dependency Parsing

2022 ◽  
Vol 21 (3) ◽  
pp. 1-19
Author(s):  
Sujata Rani ◽  
Parteek Kumar
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Minimum Distance ◽  
Dependency Graph ◽  
Media Analysis ◽  
Product Reviews ◽  
Dependency Parsing ◽  
Social Media Analysis ◽  
Dependency Parser ◽  
Sentiment Word

In this paper, an aspect-based Sentiment Analysis (SA) system for Hindi is presented. The proposed system assigns a separate sentiment towards the different aspects of a sentence as well as it evaluates the overall sentiment expressed in a sentence. In this work, Hindi Dependency Parser (HDP) is used to determine the association between an aspect word and a sentiment word (using Hindi SentiWordNet) and works on the idea that closely connected words come together to express a sentiment about a certain aspect. By generating a dependency graph, the system assigns the sentiment to an aspect having a minimum distance between them and computes the overall polarity of the sentence. The system achieves an accuracy of 83.2% on a corpus of movie reviews and its results are compared with baselines as well as existing works on SA. From the results, it has been observed that the proposed system has the potential to be used in emerging applications like SA of product reviews, social media analysis, etc.

scholarly journals An Investigation into the Attitudes of Archivists towards the Use of Social Media at Archives New Zealand

2021 ◽  
Author(s):  
◽  
Leah Johnston
Keyword(s):  
Social Media ◽  
New Zealand ◽  
Thematic Analysis ◽  
Research Problem ◽  
Media Analysis ◽  
Structured Interviews ◽  
Social Media Analysis ◽  
User Attitudes ◽  
Use Of Social Media

<p>Research problem: The purpose of this case study was to identify the attitudes of reference archivists at Archives New Zealand towards the use of social media. Analysis of the results aimed to determine whether attitudes expressed were affecting the organization’s current use of social media. Methodology: Thematic analysis was employed to identify themes of attitudes expressed by the archivists during semi-structured interviews. In turn content analysis was undertaken to determine Archives New Zealand’s current use of social media. Results: Analysis of the data showed that archivists were able to see the opportunities that the use of social media could bring. Although some concerns were expressed the overall impression given that it would be used in future but first a strategic plan need be put in place. Implications: Although results provide some insight, as a relatively small study it would be beneficial for further research to be undertaken. Additionally, a similar study of user attitudes would provide a more balanced view of the use of social media at Archives New Zealand.</p>

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