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Your Advanced Twitter stalking tool

License: GNU Affero General Public License v3.0

Python 27.27% Jupyter Notebook 60.86% CSS 0.14% HTML 11.72%
machine-learning nlp osint sentiment-analysis sentiment-classification social-media social-media-analysis social-media-mining text-classification text-classifier twint twitter twitter-api

tia's Introduction

๐Ÿ‘‹ Hi, I'm Umit

GitHub User's stars Views

Interests: Machine learning (time-series and graphs), Explainable AI, Social networks, Venture capital

Currently: Learning Lisp & CUDA

โญ My favorite projects

๐Ÿ•ต๏ธ reddit-detective: Detect political disinformation campaigns, discover how ideas spread between communities, find "cyborg-like" activities carried out by bots and more in Reddit Downloads

๐Ÿฐ Jomini: What if Byzantines had more soldiers in 1453? You can model this and many other historical battles Downloads

๐Ÿค TIA is an advanced Twitter stalking/analysis tool powered by machine learning.

๐Ÿ’ฐ Trying a New Fraud Detection Approach for Trust Networks While trying to detect fraud rings in the bitcoin-otc network, I came up with an individual fraud detection approach which is better than 9 of 10 well-known network-based fraud detection algorithms for this problem. (At least for this data set, I'll try it in different datasets and tune the models when I have time for that)

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tia's Issues

How to run scrape_user.py?

@umitkaanusta I cloned the repository and use it as CWD. The README barely elaborates on how to run the script, which is why I opened the issue. Let me walk you through my process.

  1. Clone the repository
    git clone https://github.com/umitkaanusta/TIA

  2. Change directory into repository
    cd TIA

  3. Install required packages from pip
    pip install -r requirements.txt

  4. At this point I should be ready to run the script.

  5. Change directory into tia/stalk
    cd tia/stalk

  6. Open up scrape_user.py in text editor.
    micro scrape_user.py

  7. Instructions in the bottom of the file are as follows:

        \# Create a user instance with the username, like uka = User("umitkaanusta")
        \# Then scrape_user(uka)
    
    
  8. (This is where the confusion begins.) I Add the following to the top of the file below the import statements.
    user = User("some_username")
    scrape_user(user)

  9. Save and close out editor.

  10. Execute the scrape_user.py script.
    python3 scrape_user.py

  11. I receive the following error:

Traceback (most recent call last): File "scrape_user.py", line 1, in from tia.stalk import URL_ROOT ModuleNotFoundError: No module named 'tia.stalk'

conclusion

What am I doing wrong? Why is this not working?

Was this written in Windows by any chance?

Originally posted by @anoduck in #3 (comment)

What earthly use you can make of TIA

Hey folks, it's been a while and the dependency twint (used to scrape Twitter) does not work anymore, and the official Twitter API is quite expensive. So, TIA won't work anymore.

But if you get ahold of some twitter data, you can use the models/methods here as a first step (the gender model is quite basic but still decent). The webpage on flask could also be helpful if you need some boilerplate code to visualize your results.

It was my first hit project on GH, surely learned a lot, and thank you for your support!

All the best,
Umit

Dependencies

Create a requirements.txt file for other developers to be able to run locally.
And, optionally, add dependencies section to README.md

A little help running the initial script for scraping: scrape_user.py

Barely a novice programming python, and I am a little lost on how exactly the script is intended to be run. I opened up pycharm and attempted to add the variable then execute the script with the python console:

usr = User(some_user)
scrape_user(usr)

As you probably surmised this did not work in the least. Then I added the variable user = "some_user", then attempted to execute the script and this did not work as well. Adding user = User("some_user") failed as well. So, I am lost as to how exactly this is supposed to be run. A little help would be greatly appreciated.

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