89 lines
3.1 KiB
Markdown
89 lines
3.1 KiB
Markdown
---
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title: Toxicity Analysis in YouTube Live Chat
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---
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A little analysis and experiment on a flock of toxic people.
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# Why
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The motivation is straightforward; I just feel sad when they sound suffered from toxic chats. The goal is also straightforward: design an automated system to spot toxic chat and quarantine them.
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# Data, Data, Data
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> I can't make bricks without clay.
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> — Sherlock Holmes
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I need a myriad of live chat comments and moderation events for this.
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Unfortunately, YouTube API does not offer a way to retrieve these kinds of events in real time. Which is crucial because live streams are only place we can observe moderators' actions (deletion and BAN). Once it gets archived, these activities are no longer observable.
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## Collecting Crusts
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So, I ended up developing a library to accumulate events from a YouTube live stream, with a fancy CLI app mimics live chat. It accepts YouTube video id and save live chats in [JSON Lines](https://jsonlines.org/) format:
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```bash
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collector <videoId>
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```
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A line with white text is a normal chat, red text is a ban event, and yellow text is a deletion event.
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I know, that's not scalable at all. A new live stream comes in, I copy and paste video id into the terminal and run the script. How sophisticated.
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## Make Bread Rise
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Thankfully, there's a great web service around Hololive community: [Holotools](https://hololive.jetri.co). They operate an API that gives us an index of past, ongoing, and upcoming live streams from Hololive talents.
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Here I divided my system into two components: Scheduler and workers. Scheduler periodically checks for newly scheduled live streams through Holotools API and create a job to be handled by workers. Workers are responsible for handling jobs and spawning a process to collect live chat events.
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I run the cluster for a while and by far it hoards approximately one million comments per day. Now I could reliably run my own bakery.
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# Look Before You Leap
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Okay now there are five million chats sitting on MongoDB store. Let's take a close look at these before actually starting to build a model.
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## Troll's Behavior
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## By talent
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## By language
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# Creating Dataset
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## Labelling Spam & Toxic Chat
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### Utilizing Moderators' Activities
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### Introducing Normalized Co-occurrence Entropy
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$$
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NCE(T) = \frac{N_T}{RLE_{string}(BWT(T))}
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$$
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$$
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BWT[T,i] = \begin{cases} T[SA[i]-1], & \text{if }SA[i] > 0\\ \$, & \text{otherwise}\end{cases}
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$$
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Shannon Entropy is not enough. So I combined the ideas of [Burrows-Wheeler Transform](https://en.wikipedia.org/wiki/Burrows%E2%80%93Wheeler_transform) and [Run-length Encoding](https://en.wikipedia.org/wiki/Run-length_encoding) to formulate a new entropy which represents "spamminess" of given text.
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### Browser Extension
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## Sentence Encoding
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Here's a [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) visualization for output of Sentence Transformer. Blue dots are spam and orange dots are normal chats.
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# Learn
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## Gradient Boosting
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## Neural Networks
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# Future
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When it's ready, I'm going to publish a dataset and pre-trained model used in this experiment.
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