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README.md

README.md

De-escalation Twitter Bot

Aim: This project aims to explore the topics of context-free generation, topic and sentiment analysis, paraphrasing and the use of principles of non-violent communication for de-escalation of online discourse. Powered by Python machine learning backend and a Twitter bot written in Node.js (using Seed for seeded context-free generation), this bot will try to participate in heated Twitter threads and resolve conflicts.

Collaborators: Alexander Rossa, Frederik De Bleser

Getting Started:

Download:

To download the source code and supporting files for this bot, you can just fork this repository or download it as a zip.

The bot will still not be able to be run out of the box though, as the execution is bound to having pretrained models for topic analysis and anger analysis. Trained models and pickled tokenizers and label encoders that work with the source code in this repository can be downloaded from Amazon AWS:

After downloading these, create a directory models in the python-backend subdirectory and move these models there.

Running Locally:

This project is designed as a NodeJS frontend with Python microservices backend that takes in a tweet, analyses it using pretrained neural network models and sends back results of analysis. The backend is running on a Flask server. To run the whole project locally, firstly the Flask server needs to be launched.

Open you command line (or terminal), navigate to the python-backed subdirectory and launch the Flask application. On Windows, this would look like this:

>> set FLASK_APP=app.py
>> flask run

On a different OS, you should be fine by just switching keyword 'set' for 'export'.

Once the Flask server is up and running, the bot can be launched and use the services provided by backend. Before we launch the bot itself, we need authentication file that the bot can use. You need to create this one yourself and call it auth.js. This should contain your Twitter authentication. You can get these from Twitter when creating a Twitter app. There are plenty of tutorials out there on how to do this. After you get your Twitter credentials, the auth.js file should look like this:

module.exports = {
    consumer_key: 'YOUR_CONSUMER_KEY',
    consumer_secret: 'YOUR_CONSUMER_SECRET',
    access_token: 'YOUR_ACCESS_TOKEN',
    access_token_secret: 'YOUR_ACCESS_SECRET'
}

With authentication successfully mastered, we can launch the bot. Open another command line window and navigate to node-frontend subdirectory. From here you can launch the bot by calling it with Node.

>> node bot.js

That's it! The bot is now up an running. Unless you modified the source code, the bot now listens for #DeescalationBot hashtag and is ready to respond.

Remote Deployment:

Instructions for remote deployment of the bot (for example on Heroku) will be added soon. The deployment should be quite standard but needs adjusting (point to remotely stored models rather than those on disk, changing paths here and there, adjusting Procfiles and so on). If you have done this kind of thing before it should be quite straightforward and you don't have to wait for this part of tutorial to be completed.

Customizing the Bot:

It is very likely that you'll want to customize the output of the bot or the way it responds or any of the other things there are to be customized. For this, please, look into respective subdirectories depending on what part of the bot you want to customize, as that is where the information you need is provided. If you want to adjust something with Machine Learning backend and analysis of tweets, go to the python-backend subdirectory. If you want to change how the responses are generated or how the bot acts or what it reacts to, go to the node-frontend subdirectory.

Bot Overview:

Machine Learning Backend (Python):

The backend of the bot consists of two main scripts, residing in python-backend subdirectory. These are topic_analyser.py and anger_analyser.py. These two scripts provide code for loading in a pretrained model and supporting python objects (such as Tokenizer) and they provide method analyse(tweet) that takes in a Tweet and returns an analysis of it. Detailed information can be found in the python-backend subdirectory.

Twitter Bot Frontend (Node.js):

The frontend is fueled by Javascript. The majority of the relevant code can be seen in bot.js file which takes care of both communicating with backend via microservices and of finding, reading and sending out tweets. The bot relies on a couple of Node modules, most notably seedtext, which has been co-developed along with the bot. Detailed information can be found in the node-frontend subdirectory.

Seed:

Seed is a JavaScript application (also available as a Node.js module - SeedText used for seeded pseudo-random procedural content generation. It uses its own microlanguage for guiding the generation and the whole documentation for this can be seen in its GitHub repository.