Suicidal individuals are extremely likely to socially isolate, and, in a global pandemic, social isolation has become the norm for everyone. So, we turned to social media in hopes of leveraging user data to supplement the now-weakened "watch-dog" role that friends and family normally play in our lives. As a proof of concept, we developed an opt-in Reddit bot service that uses an ML text classification model to analyze a user's recent post history for suicidal sentiment. If recent posts indicated a high-risk for suicide, the user-defined emergency contacts are notified. The model can be trained on and expanded to other social media platforms easily.
Check out u/Watch-Bot
Ben Lucero (Pitzer College), Ryan Hunter, Nayana Tiwari, Julian Rice (rest from Cal Poly San Luis Obispo)
The most recent releases of sklearn, matplotlib, keras, praw, and seaborn are required to run the Watch-Bot
The Watch-Bot collects user posts and combines them as one document. Then, it vectorizes the data and attempts to classify the posts as either 1: high-risk for suicide or 0: not a high-risk for suicide.
The Watch-Bot currently implements a variety of classifying models to do this classification, and, in production, currently uses the highest preforming model: Multinomial Naive Bayesian classification.
python3 generate_model.py will walk you through generating an available non-nueral-net model. It will also output the models results against test data.
python3 main.py will train a neural network, built in Keras. These models are implemented in src/keras_model.py. Use the --modelname argument to specify a particular model architecture:
A basic fully-connected architecture from Keras demo code.
Another fully-connected arch, but much wider and deeper (6 layers, depending on how you count)
A deep convolutional network
A dense network with two inputs. The first is the standard input of the actual text, that is then processed the same as the big-dense network. The second input is a six-number vector generated by IBM Watson sentiment and emotion analysis representing the positive/negative sentiment and various emotion scores of the text (this data is stored in the 'data/pos_sent_emot.json' and 'data/neg_sent_emot.json'). The network learns relationships within these two inputs, and then combines them and learns the relationship between them at the end.
To run Watch-Bot, you'll need reddit api credentials in a file named praw.ini in your the base directory of this repository. For demos and tests, please refer to our slide deck.
The bot can be run with python3 bot.py and is written as though it were continually on. Users are maintained between instances with database, A test user database can be configured with python3 test_cases.py.
The different python files for the models are an un-needed leftover of hacking this together. They could easily be abstracted to a single object initialized based on desired model. The neural-net models could also be attached to the same interface.