• Balaji Chaugule
• Ankita Sambhare
• Pooja Khedkar
• Ashiwini Gaikwad

Our perspective, latest and modern to the apply on Social Network Human Addiction Detection, does not depend on demonstrative of those psychological elements through questionary. as a substitute, we recommend a machine learning frame, namely, Social Network Human Addiction Detection (SNHAD), with the aim of exploits characteristic take out from Social Network records to exactly recognize probable occurrence of SNHADs. The new users register on the social network like twitter and then login. The client has to posting their observation on the Social Network and be capable to keep an eye on their progression and find associated anomalies and the organization then retrieve the irregularity thing of that particular post from database which the user has posted and these posts are displayed in the anomaly list.

Twitter text stream, blog and microblogs, emerging, anomalies.

" Human Social Network Addiction Detection using Social Networking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.278-281, May-2019, Available :http://www.jetir.org/papers/JETIRBO06053.pdf

" Human Social Network Addiction Detection using Social Networking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp278-281, May-2019, Available at : http://www.jetir.org/papers/JETIRBO06053.pdf