├── requirements.txt ├── twitter_default_all.json.zip ├── project_export_2023_04_01_12_05_22.zip ├── issue_scraper.py ├── .gitignore ├── README.md └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | requests -------------------------------------------------------------------------------- /twitter_default_all.json.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/code-kern-ai/twitter-issues-classifier/HEAD/twitter_default_all.json.zip -------------------------------------------------------------------------------- /project_export_2023_04_01_12_05_22.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/code-kern-ai/twitter-issues-classifier/HEAD/project_export_2023_04_01_12_05_22.zip -------------------------------------------------------------------------------- /issue_scraper.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import json 3 | 4 | PAT = "add-your-PAT-here" # see https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token 5 | owner = "twitter" 6 | repo = "the-algorithm" 7 | 8 | url = f"https://api.github.com/repos/{owner}/{repo}/issues" 9 | headers = {"Authorization": f"Bearer {PAT}"} 10 | 11 | all_issues = [] 12 | 13 | while url: 14 | response = requests.get(url, headers=headers) 15 | if response.status_code == 200: 16 | issues = response.json() 17 | all_issues.extend(issues) 18 | if "next" in response.links: 19 | url = response.links["next"]["url"] 20 | else: 21 | url = None 22 | else: 23 | print(f"Failed to retrieve issues (status code {response.status_code}): {response.text}") 24 | break 25 | 26 | issues_reduced = [] 27 | for issue in all_issues: 28 | issue_reduced = { 29 | "title": issue["title"], 30 | "body": issue["body"], 31 | "html_url": issue["html_url"], 32 | "reactions_laugh": issue["reactions"]["laugh"], 33 | "reactions_hooray": issue["reactions"]["hooray"], 34 | "reactions_confused": issue["reactions"]["confused"], 35 | "reactions_heart": issue["reactions"]["heart"], 36 | "reactions_rocket": issue["reactions"]["rocket"], 37 | "reactions_eyes": issue["reactions"]["eyes"], 38 | } 39 | issues_reduced.append(issue_reduced) 40 | 41 | with open("twitter-issues.json", "w") as f: 42 | json.dump(issues_reduced, f) 43 | 44 | print(f"Retrieved {len(all_issues)} issues and saved to twitter-issues.json") 45 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # twitter-issues-classifier 2 | Ok, you all most likely heard it. Twitter went open-source. That's amazing. Curious as I am, I wanted to dive into their [repository](https://github.com/twitter/the-algorithm). 3 | 4 | When looking into their issues list, I was laughing out loud. Check this: 5 | 6 | ![Funny issues](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/fauqrbxiavcojqyyp27n.png) 7 | 8 | GitHub users are making fun on the whole release, and turn the issues list into a jokes section. 9 | 10 | As an engineer on the dev team of Twitter, however, I would be really annoyed. Differentiating between issues of trolls and non-trolls is now a new todo on their list. So let's try to help them. I'm going to show a first, very simple version of a classifier for identifying troll-issues in the Twitter repo. Of course, I'm sharing the work on GitHub as well. Here's the [repo](https://github.com/code-kern-ai/twitter-issues-classifier). 11 | 12 | --- 13 | 14 | ## Getting the data 15 | 16 | I've scraped the issues with a simple Python script, which I also shared in the repo: 17 | ```python 18 | import requests 19 | import json 20 | 21 | PAT = "add-your-PAT-here" # see https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token 22 | owner = "twitter" 23 | repo = "the-algorithm" 24 | 25 | url = f"https://api.github.com/repos/{owner}/{repo}/issues" 26 | headers = {"Authorization": f"Bearer {PAT}"} 27 | 28 | all_issues = [] 29 | 30 | while url: 31 | response = requests.get(url, headers=headers) 32 | if response.status_code == 200: 33 | issues = response.json() 34 | all_issues.extend(issues) 35 | if "next" in response.links: 36 | url = response.links["next"]["url"] 37 | else: 38 | url = None 39 | else: 40 | print(f"Failed to retrieve issues (status code {response.status_code}): {response.text}") 41 | break 42 | 43 | issues_reduced = [] 44 | for issue in all_issues: 45 | issue_reduced = { 46 | "title": issue["title"], 47 | "body": issue["body"], 48 | "html_url": issue["html_url"], 49 | "reactions_laugh": issue["reactions"]["laugh"], 50 | "reactions_hooray": issue["reactions"]["hooray"], 51 | "reactions_confused": issue["reactions"]["confused"], 52 | "reactions_heart": issue["reactions"]["heart"], 53 | "reactions_rocket": issue["reactions"]["rocket"], 54 | "reactions_eyes": issue["reactions"]["eyes"], 55 | } 56 | issues_reduced.append(issue_reduced) 57 | 58 | with open("twitter-issues.json", "w") as f: 59 | json.dump(issues_reduced, f) 60 | 61 | print(f"Retrieved {len(all_issues)} issues and saved to twitter-issues.json") 62 | ``` 63 | 64 | Of course, these days, I didn't write the code for this myself. ChatGPT did that, but you all already know that. 65 | 66 | I decided to reduce the downloaded data a bit, because much of the content didn't seem to be relevant to me. Instead, I wanted to just have the URL to the issue, the title and body, and some potentially interesting metadata in form of the reactions. 67 | 68 | An example of this looks as follows: 69 | ```json 70 | { 71 | "title": "adding Documentation", 72 | "body": null, 73 | "html_url": "https://github.com/twitter/the-algorithm/pull/838", 74 | "reactions_laugh": 0, 75 | "reactions_hooray": 0, 76 | "reactions_confused": 0, 77 | "reactions_heart": 0, 78 | "reactions_rocket": 0, 79 | "reactions_eyes": 0 80 | }, 81 | ``` 82 | 83 | ## Building the classifier 84 | 85 | With the data downloaded, I started [refinery](https://github.com/code-kern-ai/refinery) on my local machine. With refinery, I'm able to label a little bit of data and build some heuristics to quickly test if my idea works. It's open-sourced under Apache 2.0, you can just grab it and try along. 86 | 87 | Simply upload the `twitter-issues.json` file we just created: 88 | ![Upload data](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/9qo16d8ceqz608tjrw77.png) 89 | 90 | For the `title` and `body` attributes, I added two `distilbert-base-uncased` embeddings directly from Hugging Face. 91 | ![Project settings](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/5a1dgvv8qms3p9ur35xo.png) 92 | 93 | After that, I set up three labeling tasks, of which for now only the `Seriousness` task is relevant. 94 | ![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/nox30jrtt0hjxwbswr48.png) 95 | 96 | Diving into the data, I labeled a few examples to see how the data looks like and to get some reference labels for my automations I want to build. 97 | ![Labeling data](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3jd82pdkx36t0fma4kk3.png) 98 | 99 | I realized that quite often, people are searching for jobs in issues. So i started building my first heuristic for this, in which I use a lookup list that I created to search for appearances of job-terms. I'm going to later combine this via [weak supervision](https://www.youtube.com/watch?v=8TusRTqp9uQ&ab_channel=KernAI) with other heuristics to power my classifier. 100 | 101 | ![Job search heuristic](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0ckwz2bnmvufgpc3tffd.png) 102 | 103 | For reference, this is how the lookup lists looks like. Terms are automatically added while labeling spans (which is also why i had three labeling tasks, one for classification and two for span labeling), but I could also have uploaded a CSV file of terms. 104 | ![Lookup list job terms](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ujx1p9nl1tmxla6t87it.png) 105 | 106 | As I also already labeled a bit of data, I created a few active learners: 107 | ![Active Learner](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/33rx85uw1tbmha1tpv62.png) 108 | 109 | With weak supervision, I can easily combine this active learner with my previous job search classifier without having to worry about conflicts, overlaps and the likes. 110 | 111 | Also I noted a couple of issues with just a link to play chess online: 112 | ![Play chess](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/g3fqgvwamhgzs0n78k4v.png) 113 | 114 | So i added a heuristic for detecting links via spaCy. 115 | ![Title is link](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ffrrs2zfjqli9j90o1wf.png) 116 | 117 | Of course, I also wanted to create a GPT-based classifier, since this is publicly available data. However, GPT seems to be down while I'm initially building this :( 118 | ![GPT-down](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/lfu1tuoe68be0rbj4p5t.png) 119 | 120 | After circa 20 minutes of labeling and working with the data, this is how my heuristics tab looked like 121 | ![All heuristics](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/7uvk32nddy6nmyfaqap1.png) 122 | 123 | So there are mainly active learners, some lookup lists and regular-expression like heuristics. I will add GPT in the comments section as soon as I can access it again :) 124 | 125 | Now, I weakly supervised the results: 126 | ![Distribution](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ktxp9dfxreq3zl9yf7vb.png) 127 | 128 | You can see that the automation already nicely fits the distribution of trolls vs. non-trolls. 129 | 130 | I also noticed a strong difference in confidence: 131 | ![Confidence distribution](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/r214w76egjq997uumjun.png) 132 | 133 | So I headed over to the data browser and configured the confidence so I only see the records with above 80% confidence. 134 | ![Data browser](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/24qgjr6ud74zlejqabd9.png) 135 | 136 | Notice that in here, we could also filter by single heuristic hits, e.g. to find records where different heuristics vote different labels: 137 | ![Heuristics filtering](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/tpkcvwqi362oucgzhvz2.png) 138 | 139 | In the dashboard, I now filter for the high confidence records and see that our classifier is performing quite good already (note, this isn't even using GPT yet!): 140 | ![Confusion matrix](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/6ohc6kwjjmuchm3qyoq0.png) 141 | 142 | ## Next steps 143 | I exported the project snapshot and labeled examples into the [public repository](https://github.com/code-kern-ai/twitter-issues-classifier) (`twitter_default_all.json.zip`), so you can play with the bit of labeled data yourself. I'll continue on this topic the next days, and we'll add a YouTube video for this article for a version 2 of the classifier. There certainly are further attributes, we can look into, such as taking the length of the body into account (I already saw that shorter bodys typically are troll-like). 144 | 145 | Also, keep in mind that this is an excellent way to benchmark how power GPT can add for your use case. Simply add it as a heuristic, try a few different prompts, and play with excluding or adding it from your heuristics in the weak supervision procedure. For instance, here, I excluded GPT: 146 | 147 | ![All heuristics](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/7uvk32nddy6nmyfaqap1.png) 148 | 149 | --- 150 | 151 | I'm really thrilled about Twitter going open-source with their algorithm, and I'm sure it will add a lot of benefits. What you can already tell is due to the nature of Twitter's community, issues are often written by trolls. 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