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Instagram Bot Multimodal Classification Model

Created for ECS271 Machine Learning project of classifying Instagram accounts as controlled by a real or fake user.

Project by Amy Vu, Aaron Nguyen, and Arisa Cowe

Files Included

data-collect.py: script that utilizes Instaloader to gather data from public profiles on Instagram when provided with their username.

all .csv files: contains username and classification label for our script to web scrape from

data folder: all subfolders contain the gathered data for each account scraped from Instagram. includes: username, number of followers, number of accounts following, profile picture, biography, post images, and post captions

ECS271.ipynb: jupyter notebook containing all code for data extraction and preprocessing, individual unimodal models, and final multimodal model

Results

Average across 5 validation tests

Numerical: F1 - 0.5, Accuracy - 0.627

Profile Picture: F1 - 0.638, Accuracy - 0.712

Posts: F1 - 0.815, Accuracy - 0.831

Textual: F1 - 0.881, Accuracy - 0.881

Average: F1 - 0.852, Accuracy - 0.864

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Dataset collection and multimodal model for Instagram bot classification

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