Special issue on computational intelligence for social media data mining and knowledge discovery

2021 ◽  
Vol 37 (2) ◽  
pp. 658-659
Author(s):  
Ying Li ◽  
R. K. Shyamasundar ◽  
Xinheng Wang
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Knowledge Discovery ◽  
Computational Intelligence ◽  
Special Issue ◽  
Social Media Data ◽  
Media Data

A Survey of Social Media, Big Data, Data Mining, and Analytics

2018 ◽  
Vol 03 (03) ◽  
pp. 1850003 ◽  
Author(s):  
Jared Oliverio
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Environmental Education ◽  
Family Businesses ◽  
Large Percentage ◽  
Specific Behavior ◽  
Social Media Data ◽  
Media Applications ◽  
Media Data

Big Data is a very popular term today. Everywhere you turn companies and organizations are talking about their Big Data solutions and Analytic applications. The source of the data used in these applications varies. However, one type of data is of great interest to most organizations, Social Media Data. Social Media applications are used by a large percentage of the world’s population. The ability to instantly connect and reach other people and companies over distributed distances is an important part of today’s society. Social Media applications allow users to share comments, opinions, ideas, and media with friends, family, businesses, and organizations. The data contained in these comments, ideas, and media are valuable to many types of organizations. Through Data Mining and Analysis, it is possible to predict specific behavior in users of the applications. Currently, several technologies aid in collecting, analyzing, and displaying this data. These technologies allow users to apply this data to solve different problems, in different organizations, including the finance, medicine, environmental, education, and advertising industries. This paper aims to highlight the current technologies used in Data Mining and Analyzing Social Media data, the industries using this data, as well as the future of this field.

scholarly journals Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining

2020 ◽  
Vol 17 (14) ◽  
pp. 4988 ◽  
Author(s):  
Diya Li ◽  
Harshita Chaudhary ◽  
Zhe Zhang
Keyword(s):  
Data Mining ◽  
Social Media ◽  
San Francisco ◽  
Learning Algorithm ◽  
The United States ◽  
Spatiotemporal Pattern ◽  
Social Media Data ◽  
Clinical Patient ◽  
Stress Symptoms ◽  
Media Data

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people’s stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people’s risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.

Fad or Here to Stay: Predicting Product Market Adoption and Longevity Using Large Scale, Social Media Data

2013 ◽  
Author(s):  
Suppawong Tuarob ◽  
Conrad S. Tucker
Keyword(s):  
Social Media ◽  
Knowledge Discovery ◽  
Large Scale ◽  
Product Information ◽  
Product Market ◽  
Knowledge Discovery In Databases ◽  
Product Reviews ◽  
Product Model ◽  
Social Media Data ◽  
Media Data

The authors of this work propose a Knowledge Discovery in Databases (KDD) model for predicting product market adoption and longevity using large scale, social media data. Social media data, available through sites such as Twitter® and Facebook®, have been shown to be leading indicators and predictors of events ranging from influenza spread, financial stock market prices, and movie revenues. Being ubiquitous and colloquial in nature allows users to honestly express their opinions in a unified, dynamic manner. This makes social media a relatively new data gathering source that can potentially appeal to designers and enterprise decision makers aiming to understand consumers response to their upcoming/newly launched products. Existing design methodologies for leveraging large scale data have traditionally relied on product reviews available on the internet to mine product information. However, such web reviews often come from disparate sources, making the aggregation and knowledge discovery process quite cumbersome, especially reviews for poorly received products. Furthermore, such web reviews have not been shown to be strong indicators of new product market adoption. In this paper, the authors demonstrate how social media can be used to predict and mine information relating to product features, product competition and market adoption. In particular, the authors analyze the sentiment in tweets and use the results to predict product sales. The authors present a mathematical model that can quantify the correlations between social media sentiment and product market adoption in an effort to compute the ability to stay in the market of individual products. The proposed technique involves computing the Subjectivity, Polarity, and Favorability of the product. Finally, the authors utilize Information Retrieval techniques to mine users’ opinions about strong, weak, and controversial features of a given product model. The authors evaluate their approaches using the real-world smartphone data, which are obtained from www.statista.com and www.gsmarena.com.

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