Identifying trolls and bots on Reddit with machine learning (Part 2) - Identificando trolls y bots en reddit con Machine Learning

• Reddit-Kafka-Producer

  1 3 0.0 JavaScript
  

We are using an event-driven architecture that consists of a process that downloads data from Reddit and pushes it in a Kafka queue. We then have a Kafka consumer that writes the data into a Redshift data warehouse in batches. We wrote a Kafka producer application to download the comments from the list of bots and trolls. As a result, our data warehouse contains not only the data from the known bots and trolls, but also real-time comments from the politics subreddit.

• gpt-2

  63 21,111 2.5 Python

  Code for the paper "Language Models are Unsupervised Multitask Learners"

  

Troll and bot detection is a relatively new field. Historically, companies have employed human moderators to detect and remove content that’s inconsistent their terms of service. However, this manual process is expensive, plus it can be emotionally tiring for humans to review the worst content. We will quickly hit the limits of human moderator efficaciousness as new technologies like OpenAI GPT-2 natural language generation are unleashed. As bots improve, it is important to employ counter technologies to protect the integrity of online communities.

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• Reddit-Dashboard-ML

  1 11 0.0 Jupyter Notebook
  

recent_num_comments recent_num_last_30_days recent_avg_no_follow recent_avg_gilded recent_avg_responses recent_percent_neg_score recent_avg_score recent_min_score recent_avg_controversiality recent_avg_ups recent_avg_diff_ratio recent_max_diff_ratio recent_avg_sentiment_polarity recent_min_sentiment_polarityFor more information on what these fields are and how they are calculated, see the code in our Jupyter Notebooks in https://github.com/devspotlight/botidentification.

• Newman

  241 6,718 7.5 JavaScript

  Newman is a command-line collection runner for Postman

  

git push heroku masterHeroku takes care of the hassle of downloading requirements, building the API, setting up a web server, routing, etc. We can now access our API at this URL and use Postman to send a test request:

• scikit-learn

  81 58,046 9.9 Python

  scikit-learn: machine learning in Python

  

Our next step is to create a new machine learning model based on this list. We’ll use Python’s excellent scikit learn framework to build our model. We’ll store our training data into two data frames: one for the set of features to train in and the second with the desired class labels. We’ll then split our dataset into 70% training data and 30% test data.

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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