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Detection of Fake Profile in Online Social Networks Using Machine Learning
Detection of Fake Profile in Online Social Networks Using Machine Learning
Information
The amount of research work is very low to recognize counterfeit characters made by people
Creating false account
Reasoning
Peoples and groups who forge their identities on social media platforms are striving to spread chaos in our society.
We are not expecting that people will give their accurate details in the privacy policies of social media platforms
Now a day’s fake accounts can be bought online at a very less cost and can be given to the customer via crowdsourcing services. Now a day it is easier to buy Twitter and Instagram followers and likes online
Alter the credibility of any account so that it can be used to spread false news, spreading rumours. Scandalize someone’s character polarising opinions
Related works
Techniques which have been applied or similar to detect, identify and eliminate fake accounts
If a certain message contains these words or number of words these are addressed to as spam.
Main drawback is that the process of developing new words are easy and constant and the use of shortened words are becoming more common on platform for example lol
Facebook uses algorithms which can identify bots using the number of friends for deceptiveness which could either be related to relationships history or tagging.
Successful for identifying bot accounts by have not been successful in identifying fake accounts by humans
Unsupervⅰsed ML
Technique "Bunching"
Could be utilized for distinguised bot
Information not labeled, but are assembled in view of closeness
Administered ML
Requires dataset of highlights with a name arranging each column or result.
Hⅰghlⅰghts are therefore information utilized by regulated ML models to foresee a result. These highlights can be the characteristics discovered by means of APIs
Traits example (Twitter)
name
screenname
created
friends_count
followers_count
language
language
listed_count
profile_image
status_count
location
timezone
utc_offset
Designed highlights differentiated to 3 gatherings
potraying the data of the accounts
the connection between record and others
record behavior and message
To recognize counterfeit fake bot accounts in SOCIAL MEDIA PLATFORMSs, there are combinations developed by connecting various engineered features to machine learning models
Cresci et al in their model shows that the characteristic features of the record is enough to distinguish the record between the normal or bots
Gupta et al. conducted and proposed, the recurrence and types of messages and on what particular time of day, gives more data pertinent to trickiness than the characteristics features of the record itself [11].
Meta
2018
Author
Naman Singh
Tushar Sharma
Abha Thakral
Tanupriya Choudhury
University
Amity University Uttar Pradesy
University of Petroleum and Energy Studies
Goals
Recognize fake accounts
Result