scholarly journals Mining Public Opinion about Economic Issues

2018 ◽  
Vol 9 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Amir Karami ◽  
London S. Bennett ◽  
Xiaoyun He
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Text Mining ◽  
Opinion Mining ◽  
Social Media Data ◽  
Economic Issues ◽  
Election Prediction ◽  
Us Presidential Election ◽  
Mining Methods ◽  
Media Data

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.

Using social media data in diabetes care: Bridging the conceptual gap between health providers and patients (Preprint)

2021 ◽  
Author(s):  
Ru-Hsueh Wang ◽  
Yu-Wen Hong ◽  
Chia-Chun Li ◽  
Siao-Ling Li ◽  
Jenn-Long Liu ◽  
...  
Keyword(s):  
Social Media ◽  
Chinese Medicine ◽  
Sexual Dysfunction ◽  
Text Mining ◽  
Real World ◽  
Diabetes Care ◽  
Weight Reduction ◽  
Health Providers ◽  
Social Media Data ◽  
Media Data

BACKGROUND Diabetic patients with poor education about the disease may exhibit poor compliance and thus subsequently experience more complications. However, the conceptual gap between the diabetes education provided by health providers and the non-compliance of patients is still not well understood in the real world. OBJECTIVE Disclosing what people think about diabetes on social media may help to close this gap. METHODS In this study, social media data was collected from the OpView social media platform. After checking the quality of the data, we analyzed the trends in people’s discussions on the Internet using text mining. The natural language process, including word segmentation, and word count, and counting the relationships between the words. A word cloud is developed, and a clustering analyses are also performed. RESULTS There were 19,565 posts about diabetes collected from forums, community websites, and Q&A websites in 2017. The three most popular aspects of diabetes were diet (33.2%), life adjustment (21.2%), and avoiding complications (15.6%). Most of the discussions about diabetes were negative, and the top three negative ratios aspects were avoiding complications (7.60), problem-solving (4.08) and exercise (3.97). In terms of diet, the most popular topics were Chinese medicine and special diet therapy. In terms of life adjustment, financial issues, weight reduction, and a less painful glucometer were discussed the most. Furthermore, sexual dysfunction, neuropathy, nephropathy, and retinopathy were the most worrying issues in the avoiding complications area. Using text mining, we found that people care most about sexual dysfunction. Health providers care about the benefits of exercise in diabetes care, but people are mostly really concerned about sexual functioning. CONCLUSIONS A conceptual gap between health providers and diabetes patients existed in this real-world social media investigation. To spread healthy diabetic education concepts in the media, health providers might wish to provide more information related to patients actual areas of concern, such as sexual function, Chinese medicine, and weight reduction.

scholarly journals Journalists’ Use of Social Media to Infer Public Opinion: The citizens’ perspective

2021 ◽  
Author(s):  
Elizabeth Dubois ◽  
Anatoliy Gruzd ◽  
Jenna Jacobson
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Online Survey ◽  
Social Media Analytics ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Use Of Social Media ◽  
Traditional Approaches ◽  
Journalistic Practice ◽  
Media Data

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data is being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults (N=1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that: (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data; (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion; and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

scholarly journals Mining on Social Media Data: To Determine the Personality of Unrevealed Person

2019 ◽  
Vol 8 (4) ◽  
pp. 8574-8577
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Mining ◽  
Social Media Data ◽  
Data Mining Techniques ◽  
Network Discovery ◽  
The Social ◽  
Mining Methods ◽  
Using Data ◽  
Media Data

The unavoidable utilization of online networking like Facebook is giving exceptional measures of social information. Information mining methods have been broadly used to separate learning from such information. The character of the person is predicted whether he is good or not by using data mining techniques from user self-made data. Mining methods are being broadly using to separate learning from such information, main examples for them are network discovery and slant investigation. Notwithstanding, there is still a lot of room to investigate as far as the occasion information (i.e., occasions with timestamps, for example, posting an inquiry, altering an article in Wikipedia, and remarking on a tweet. These occasions react users' personal conduct standards and working forms in the social media websites.

scholarly journals (Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys

2021 ◽  
Author(s):  
Kenneth Joseph ◽  
Sarah Shugars ◽  
Ryan Gallagher ◽  
Jon Green ◽  
Alexi Quintana Mathé ◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Social Media Data ◽  
Public Opinion Surveys ◽  
Media Data

scholarly journals Detect Extreme Sentiments on Social Networks using BERT

2021 ◽  
Author(s):  
Muhammad Luqman Jamil ◽  
Sebastião Pais ◽  
João Cordeiro ◽  
Gaël Dias
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Public Opinion ◽  
Social Network ◽  
Social Networking ◽  
Online Social Networking ◽  
Social Media Data ◽  
Preceding Work ◽  
Unsupervised Approach ◽  
Media Data

Abstract Online social networking platforms allow people to freely express their ideas, opinions, and emotions negatively or positively. Previous studies have examined user’s sentiments on these platforms to study their behaviour in different contexts and purposes. The mechanism of collecting public opinion information has attracted researchers to automatically classify the polarity of public opinions based on the use of concise language in messages, such as tweets, by analyzing social media data. In this paper, we extend the preceding work [1], by proposing an unsupervised approach to automatically detect extreme opinions/posts in social networks. We have evaluated our performance on five different social network and media datasets. In this work, we use the semi-supervised approach BERT to check the accuracy of our classified dataset. The latter task shows that, in these datasets, posts that were previously classified as negative or positive are, in fact, extremely negative or positive in many cases.

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