Extract Genuine Healthcare Posts on Social Media
K. Alakananda1, K Rajendra Prasad2, C Raghavendra3
1.K Alakananda*, Dept. of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India. Email:
2Dr. K Rajendra Prasad*, Professor and Head, Dept. of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
3C Raghavendra*, Asst. Professor, Dept. of CSE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, India.
Manuscript received on September 12, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2270-2272 | Volume-8 Issue-12, October 2019. | Retrieval Number: L25491081219/2019©BEIESP | DOI: 10.35940/ijitee.L2549.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social media plays an important role in spreading the news. People who search for health online and those who rely on social media for small health issues and diet management are increasing periodically. Nowadays, food habits are greatly influenced by social media. Health-related data such as home remedies, diet management, and beauty tips are mainly focused in this paper. Such data available in social media may be genuine or might be not, just because of business strategy to promote products suggested. There is a huge scope for misleading vital content in this scenario. This paper gives an overview on how to process data using machine learning techniques and/or deep learning techniques available on social media by applying social media analytics and revile trustworthy information at one place. As well as describes how to create a platform for genuine information about health care.
Keywords: Social Media Analytics, Health Care, Machine Learning, Deep Learning.
Scope of the Article: Machine Learning