Topic Modeling in Online Social Media, User Features, and Social Networks for

2014 ◽  
pp. 2178-2191 ◽  
Author(s):  
Bo Hu ◽  
Zhao Song ◽  
Martin Ester
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Topic Modeling ◽  
Online Social Media

Assessing HPV vaccination perceptions with online social media in Italy

2019 ◽  
Vol 29 (3) ◽  
pp. 453-458 ◽  
Author(s):  
Roberto Angioli ◽  
Massimo Casciello ◽  
Salvatore Lopez ◽  
Francesco Plotti ◽  
Lidia Di Minco ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Hpv Vaccine ◽  
Hpv Vaccination ◽  
Publication Date ◽  
Main Theme ◽  
Online Social Media ◽  
Trade Name ◽  
Promotional Strategies ◽  
Underlying Mechanisms

ObjectiveBecause of the widespread availability of the internet and social media, people often collect and disseminate news online making it important to understand the underlying mechanisms to steer promotional strategies in healthcare. The aim of this study is to analyze perceptions regarding the human papillomavirus (HPV) vaccine in Italy.MethodsFrom August 2015 to July 2016, articles, news, posts, and tweets were collected from social networks, posts on forums, blogs, and pictures about HPV. Using other keywords and specific semantic rules, we selected conversations presenting the negative or positive perceptions of HPV. We divided them into subgroups depending on the website, publication date, authors, main theme, and transmission modality.ResultsMost conversations occurred on social networks. Of all the conversations regarding HPV, more than 50% were about vaccination. With regard to conversations exclusively on the HPV vaccine, 47%, 32%, and 21% were positive, negative and neutral, respectively. Only 9% of the conversations mentioned the vaccine trade name and, in these conversations, perception was almost always negative. We observed many peaks in positive conversation trends compared with negative trends. The peaks were related to the web dissemination of particular news regarding HPV vaccination.ConclusionsIn this study we have shown how mass media influences the diffusion of both negative and positive perceptions about HPV vaccines and suggest better ways to inform people about the importance of HPV vaccination.

scholarly journals Public Opinions towards COVID-19 in California and New York on Twitter

2020 ◽  
Author(s):  
Xueting Wang ◽  
Canruo Zou ◽  
Zidian Xie ◽  
Dongmei Li
Keyword(s):  
New York ◽  
Social Media ◽  
Topic Modeling ◽  
New York State ◽  
The United States ◽  
Public Opinions ◽  
Online Social Media ◽  
Twitter Users ◽  
Negative Sentiment ◽  
Over Time

Background: With the pandemic of COVID-19 and the release of related policies, discussions about the COVID-19 are widespread online. Social media becomes a reliable source for understanding public opinions toward this virus outbreak. Objective: This study aims to explore public opinions toward COVID-19 on social media by comparing the differences in sentiment changes and discussed topics between California and New York in the United States. Methods: A dataset with COVID-19-related Twitter posts was collected from March 5, 2020 to April 2, 2020 using Twitter streaming API. After removing any posts unrelated to COVID-19, as well as posts that contain promotion and commercial information, two individual datasets were created based on the geolocation tags with tweets, one containing tweets from California state and the other from New York state. Sentiment analysis was conducted to obtain the sentiment score for each COVID-19 tweet. Topic modeling was applied to identify top topics related to COVID-19. Results: While the number of COVID-19 cases increased more rapidly in New York than in California in March 2020, the number of tweets posted has a similar trend over time in both states. COVID-19 tweets from California had more negative sentiment scores than New York. There were some fluctuations in sentiment scores in both states over time, which might correlate with the policy changes and the severity of COVID-19 pandemic. The topic modeling results showed that the popular topics in both California and New York states are similar, with "protective measures" as the most prevalent topic associated with COVID-19 in both states. Conclusions: Twitter users from California had more negative sentiment scores towards COVID-19 than Twitter users from New York. The prevalent topics about COVID-19 discussed in both states were similar with some slight differences.

scholarly journals Identifying Influencers in Social Networks

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 450 ◽  
Author(s):  
Xinyu Huang ◽  
Dongming Chen ◽  
Dongqi Wang ◽  
Tao Ren
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Daily Life ◽  
Multidisciplinary Research ◽  
Multilayer Network ◽  
Online Social Media ◽  
Huge Data ◽  
Real World Datasets ◽  
Shed Light

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

Rumor Detection on Twitter Using a Supervised Machine Learning Framework

2021 ◽  
pp. 452-465
Author(s):  
Hardeo Kumar Thakur ◽  
Anand Gupta ◽  
Ayushi Bhardwaj ◽  
Devanshi Verma
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Supervised Machine Learning ◽  
Learning Framework ◽  
Online Social Media ◽  
Rumor Propagation ◽  
Multiple Classification ◽  
The Government ◽  
Rumor Detection

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work, analyzing temporal and linguistic characteristics of rumors seems to give ample time for rumor propagation. Meanwhile, with the huge outburst of data on social media, studying these characteristics for each tweet becomes spatially complex. Therefore, in this article, a two-fold supervised machine-learning framework is proposed that detects rumors by filtering and then analyzing their linguistic properties. This method attempts to automate filtering by training multiple classification algorithms with accuracy higher than 81.079%. Finally, using textual characteristics on the filtered data, rumors are detected. The effectiveness of the proposed framework is shown through extensive experiments on over 10,000 tweets.

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