The problem Authentify - Fake Social Media Profile Detection solves

The social life of everyone has become associated with online social networks. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with their rapid growth, many problems like fake profiles, and online impersonation have also grown. Fake profiles often spam legitimate users, posting inappropriate or illegal content.
Our project "Authentify" helps legitimate users to identify fake accounts on social networking sites using a machine learning model trained on previously available data. It is a user-friendly website that provides the user option to either enter the URL of profile or enter the attribute of that profile they want to check for authenticity. These attributes are passed into the machine learning model running on the backend which classifies the profile to be fake or real.

Challenges we ran into

Data collection: Gathering a large and diverse dataset of social media profiles to train a machine learning model to recognize fake profiles can be challenging. Additionally, obtaining labeled data (i.e., profiles that are definitively fake or real) can be difficult.

Feature selection: Identifying the most relevant features to include in the model to accurately detect fake profiles can be challenging. For example, there are many different types of behavior that might be indicative of a fake profile, such as a lack of engagement or a high frequency of posting.

Algorithm selection: Choosing the most appropriate machine learning algorithm for the task can be challenging, as different algorithms may perform differently depending on the specific features and data being used.

Ethical considerations: When detecting and reporting fake profiles, there are ethical considerations around privacy and freedom of speech that must be carefully navigated. Additionally, there is a risk of falsely accusing real users of being fake, which can have serious consequences.