Project Overview

My Google Summer of Code project aimed to create a Twitter bot that would be able to participate in debates around Twitter, find tweets and debates that are overly angry and try to calm them down. While creating this bot, it has also been my task to take the existing Seed application, transform it into a Node module and add some capabilities to it. This secondary aim tied in nicely with the main focus as Seed was used for response generation. All of the aims of the project were fulfilled. However, there still is potential for improvements and future work on the bot by extending its capabilities.

Twitter Bot

The Twitter bot is a mixture of a Node.js frontend and a Python backend. The backend is there for analysis of tweets using pretrained neural network models. It is implemented as a microservice, served by a Flask server routing requests to relevant analysers. Apart from sending the tweet analysis requests, the frontend provides capabilities for connecting to Twitter for reading and sending Tweets and connecting to Seed for generating responses. A detailed description of all of its parts can be seen in the official GSoC repo.

Dataset

Since the task at hand is quite specific, there was a need for custom made datasets. There are two datasets in total, one for each analysis task. The final_dataset.csv is a dataset of about 7000 tweets, downloaded, cleaned and annotated by me, including columns for text, topic and anger level. The anger_dataset.csv contains 5000+ tweets, split 50/50 into angry and non-angry ones. These are partially outsourced and partially made up of the tweets from the final_dataset. The code for creating the datasets is in the GSoC repository, along with both of the datasets.

Context

There are many approaches for tackling the de-escalation in online settings. In this project we decided to follow the rules of the non-violent communication and conflict resolution. This type of communication aims for a calm discourse with the other party, regardless of their manner of communication. The aim is to shift focus from the sentiment (anger/hate..) on to the real source of the sentiment. It is characterized by honest attempts to understand what and why the other party feels. This can be achieved for example by posing questions about the topic, giving the other party space for expressing themselves and arriving at the root of the actual problem and so on. The ability to analyse topic of the tweet, as well as having access to the historic anger levels of the communication, makes this Twitter bot quite adept at carrying out tasks like these. The generated text is made random and human-like enough with the use of Seed that the 'botness' of the bot does not pose as big of a problem.

Pretotyping

Pretotyping is a type of pre-release product testing which focuses on making sure the final product is worth making, rather than being concerned how to make that product. Before releasing the bot into the wild waters of the internet, I did a number of sessions on a Twitter account called OpenDiscussion during which I was acting like a bot myself, looking for angry tweets to respond to, taking detailed notes of each step and 'generating' responses. This was done as a way of actually seeing how people would react to our bot, what sort of interaction makes them even more angry and which actually seems to help. It also benefited the creation of the bot in making us aware of many pitfalls that there are and which the bot would have to deal with. The notes can be seen in the pretotyping subfolder of the official repo.

All in all, the pretotyping sessions were a success. Although the response rates were not that great (people mostly did not respond) this is not an issue for a bot. When the people did respond, however, in the majority of cases, the anger levels dropped or disappeared completely. Being a bot for a minute, trying to act without any preconceived notions or opinions that I have, I could see that the biggest flaws of a bot when leading discussions with humans (no preconceived notions or opinions, does not experience anger, does not get offended) are also its biggest strengths. By not getting angry itself, the bot automatically takes down the anger levels of discussion, making it more civil and calm.

Seed Improvements

Over the course of this project, the original Seed application has been made into a Node module, which is now available as seedtext at the NPM repository. Here is the GitHub repository of seedtext. It has also been extended with capabilities of conditional generation, mentioned in this GitHub issue of the original Seed app, and with the possibility to define custom methods for importing Seed sketches or using built-in methods.

Seed by default generates totally random variations of the text. Through conditional generation, this randomness can be controlled. The Twitter bot uses this functionality to vary its wording and tone depending on the range of the anger level (using words like 'upset' at anger level > 0.5 and < 0.6 and using words like 'enraged' at anger level > 0.9). This enables the generated text to be much more subtle and human-like.

Changes to the importing of sketches and transformation into Node module make Seed available to a much wider audience. Previously, the capabilities of the application were tied to the web environment, now it can be used virtually anywhere.

Future Work

There are several possible ways in which my summer work on this GSoC project could be extended. One of the possible ways would be by extending the Twitter bot capabilities. Currently, the bot lacks a comprehensive database solution for participating in true debates. There is also the possibility of improving the Seed source files, making the generated text better. Improving the capabilities of the analysis backend and the neural networks in it is also a possibility. At the moment the test set accuracy for topic analysis is around 81%. With a bigger and better dataset, this could easily go higher.

I already talked with my supervisor about possible ways in which I could collaborate on the project in the future. We talked about rewriting the original Seed repository so that it uses newly made seedtext module and adding the possibility of creating bots with the push of a button to the web application that the original Seed currently has. I am looking forward to helping make these plans reality.

Acknowledgements

At the end of this report, I would like to express my thanks to the whole team of people from CLiPS and beyond whom I had the honour to meet this summer. Special shoutout goes to my supervisor, Frederik, who has been really great over the whole duration of the internship. I am also happy that I got the chance to meet other GSoC students this year and I hope we'll stay in touch in the future. Last, but not least, I would like to thank Google for making GSoC possible, it was a wonderful experience.

During my second GSoC term I was focusing on finishing various parts of the Twitter Deescalation bot and on extending the Seed module.

Twitter bot:

• Created dataset of several thousands of Tweets for both topic prediction (keyword labeled and checked for correctness) and anger/participation classification (manually labeled)

• Improved and tested neural network models used on said dataset

• Did some pretotyping work with the bot - participating in online discussions and sort of impersonating an ideal version of the bot to see what the bot will have to deal with "in the real world" - the logs are from "bot's perspective" and closely follow the actual execution of the bot

Seed module:

• Transformed Seed into an NPM module

• Wrote up some documentation for using Seed as an NPM module

• Almost finished implementing the conditional generation, still need to do a bit of work on connecting all the outputs and do some testing for correctness of the solution

The next focus for this project will be:

• Finishing the conditional generation for Seed

• Reworking the collected dataset a bit (turns out that there were too many classes for too little data which plateaued the test set accuracy on about 60% even with heavy regularization) - I collected more data for smaller number of classes and am hand labeling it right now

• Testing and improving the bot in the real world

• Retrospectively rewriting the original Seed repository with using Seed as a Node module instead and adding the ability to easily create Twitter bots from the Seed website https://seed.emrg.be/

There are two distinct focal points in my GSoC work. The first is a functional Twitter bot, achieved by a pipeline consisting of Python machine learning backend for tweet analysis, Node.js frontend for accessing Twitter API and integration of Seed into the frontend for templated procedural text generation. The other is extending the capabilities of Seed, making it into a Node.js module (available through npm) and adding conditional generation.

The work I have done during my first month gravitated largely around the first task. The tasks that were completed include:

• Node.js frontend for manipulating Twitter API, retrieving and reacting to Tweets, sending back responses etc.

• Python ML backend for Topic Analysis and Sentiment Analysis of Tweets

• API for Python backend so that it can be accessed as a microservice rather than one monolithic deployment bundled with Node.js frontend

• Seed miniprogram for generating templated responses. This does not yet have conditional generation enabled and serves more like a testing rig for trying to generate text conforming to non-violent communication principles.

Apart from these tasks, I spent time exploring approaches for deployment and getting to grips with new technologies.

The next focus for this project will be:

• Extending Seed with conditional generation and API for easier access

• Extending Seed miniprogram with newly acquired conditional generation capabilities

• Testing and improving the bot. This may include:

• adding ML module for paraphrasing (which will enable bot to revisit topics mentioned in communication in a more natural way)

• improving quality of Seed miniprogram generation capabilities (more templates for sentences, more randomness...)

• adding new rules for participation (currently working by hashtagging)

After the first two bullet points are completed, the work on Twitter bot is basically done and is just about improving the quality of the text it is able to produce.