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A collection of scripts used to train and run a classifier to classify Twitter users as potential bots.

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# Trust Defender - A Twitter User Classifier

A collection of scripts used to train and run a classifier to classify Twitter users as potential good or bad actors.
The classifier models are based on Twitter user information and focuses heavily on the user bio description to make determinations.

-   [Prerequisites](#Prerequisites)
-   [Environment Setup](#Environment-Setup)
-   [Basic Workflow (CSV file)](#Basic-Workflow-CSV-file)
-   [Basic Workflow (DiscoverText API)](#Basic-Workflow-DiscoverText-API)
-   [Training and Re-training the model](#Training-and-Re-training-the-model)
-   [Individual Scripts](#Individual-Scripts)
-   [License](#License)

## Prerequisites

-   Python 3.6 or higher
-   If using Windows, you may need to use 32-bit python as there have been issues using the Pandas library on 64 bit
-   A Twitter Developer account with an application (https://developer.twitter.com/). You'll need the _Consumer API keys_ as well as a set of _Access token & access token secret_ pairs for the application.
-   (if connecting to the DiscoverText API), you'll need your DiscoverText API key, API secret, hostname, username, and password

## Environment Setup

-   Create your virtual environment. E.g. : `python -m venv env`
-   If necessary, make the `.sh` scripts executable: `chmod +x run-csvs-score.sh` and `chmod +x run-score-dtarchive.sh`
-   Activate your environment (`source env/bin/activate`, or on Windows: `env\Scripts\activate`)
-   Install requirements: `pip install -r requirements.txt`
-   Install nltk resources:

```
$ python
>>> import nltk
>>> nltk.download('stopwords')
>>> nltk.download('punkt')
>>> exit()
```

### Credential Setup:

In the root directory, you'll find two files: `dt_credentials.json` and `twitter_auth.json`. Fill in the requested values for each. These files will be used as input to various scripts.

## Basic Workflow (CSV file)

The `run-csvs-score` helper script will run the following scripts:

1. it runs the [extract_users_from_csvs](#script-extract_users_from_csvs-py) script to transform the list of usernames to get their Twitter information
2. it then runs the [run_nnet](#script-run_nnet-py) script across the gathered Twitter data to score the user information

```
Usage: run-csvs-score {data_directory} {base_filename_without_ext}
```

## Basic Workflow (Using the DiscoverText API)

If you have an Enterprise-level DiscoverText account with API access, you can directly pull the list of username metadata
from a DiscoverText archive or bucket.

The `run-score-dtarchive` helper script performs the following:

1. Run the [extract_users_from_dt](#script-extract_users_from_dt-py) script to export a list of usernames from an archive or bucket
2. Run the [extract_users_from_csvs](#script-extract_users_from_csvs-py) script to transform the list of usernames to get their Twitter information
3. Run the [run_nnet](#script-run_nnet-py) script across the gathered Twitter data to score the user information

```
Usage: run-score-dtarchive {data_directory} {base_filename_without_ext} {archive_id}
```

## Training and Re-training the model

_(note: there are already well-tuned, pre-trained classifiers in place - only re-train if necessary)_
The pre-trained models can be found at:

-   `/resources/model-is_good_or_bad_nnet.dat`: Keras trained file for the neural network
-   `/resources/model-is_good_or_bad_nnet.dat.h5`: Keras trained file for the neural network
-   `/resources/model-is_good_or_bad_user_desc_ngram_class.dat`: the n-gram classifier pre-trained model

There are two primary classifier types:

-   an _n-gram_ naive bayes classifier model, which uses 5,6,7,8, and 9-gram models combined and results a probability score for two classes: `bot` or `good`
-   a neural network that takes as input:
    -   `p(bot)` (from the ngram classifier)
    -   `p(good)` (from the ngram classifier)
    -   number of days the Twitter account has been active
    -   average number of statuses per day
    -   average number of followers per day
    -   average number of following per day
    -   the description length in number of individual terms
    -   the number of "lists" in the description (e.g. `god, country, president` would be considedered a "list")
    -   the number of hashtags used in the description
    -   the number of URLs found in the description

Use the script [train_ngram_classifier.py](#script-train_ngram_classifier-py) to train the n-gram classifier.

```
Usage: python train_ngram_classifier.py -i {input_csv_file} -o {output_model_file}
```

The input data in `train_ngram_classifier.py` can be specified by modifying:

-   **CLASSES**: array of classes to be use
-   **TEXT_COLUMN**: the heading label of the column that contains the text to train on
-   **CLASS_COLUMN**: the heading label of the column that contains the correct class for the record

The script [train_nnet.py](#script-train_nnet-py) is used to train the neural network.

```
Usage: python train_nnet.py -i {input_csv_file}
                            -m {ngram_classifier_model_file}
                            -n {number_training_rounds}
                            -t {testing_file_csv_path}
                            -o {output_model_file}
```

## Individual Scripts:

-   [extract_users_from_csvs.py](#script-extract_users_from_csvs-py): Get Twitter user information from CSV username list
-   [extract_users_from_dt.py](#script-extract_users_from_dt-py): Extract Twitter usernames from a DiscoverText archive or bucket
-   [gather_bio_corpus_stats.py](#script-gather_bio_corpus_stats-py): Output various statistics for bios in a corpus
-   [run_nnet.py](#script-run_nnet-py): Runs the neural network model across Twitter user information
-   [split_training_data.py](#script-split_training_data-py): Splits a training CSV file into training and test sets
-   [train_ngram_classifier.py](#script-train_ngram_classifier-py): Trains the n-gram classifier
-   [train_nnet.py](#script-train_nnet-py): Trains the neural network

### script: extract_users_from_csvs.py

Reads in a file or directory of CSV files (primarily from DiscoverText metadata item exports), and gets Twitter information from the usernames.

```
Usage: extract_users_from_csvs.py -i (input_file) -o (output_file) -c (credentials_file)
```

-   input file is a CSV of usernames (in the `Value` column by default)
-   output file is a CSV of information for each of the Twitter users' bios
-   credentials file is your `twitter_auth.json` credentials

### script: extract_users_from_dt.py

Interactive or CLI script to extract and save usernames from a DiscoverText archive or bucket (to then further use to feed into extract_users_from_csvs.py)

```
Usage: extract_users_from_dt.py -i {DiscoverText_credentials}
                                [-a {archive_id} -o {output_file}]
                                [-b {bucket_id} -o {output_file}]
```

-   input file is your `dt_credentials.json` file
-   optionally, specifying the archive id and output file will extract and run from an archive
-   or, optionally, specifying the bucket id and output file will extract and run from a bucket
-   or, running interactivly, the script will prompt for information and ask where to save the output file to

### script: gather_bio_corpus_stats.py

Gathers a bit of supurious stat information about user bio data in a corpus

```
Usage: gather_bio_corpus_stats.py -i (input_file)
```

-   input file is the CSV output from extract_users_from_csvs.py

The output will be printed to the console with the top 100 terms, top 100 URLs, and top 100 hashtagsassa.

### script: run_nnet.py

Runs the classifier and neural network, scoring Twitter data as bot or good.

```
Usage: run_nnet.py -i (input_file) -o (output_file) -n (neural_net_model) -m (ngram_model)
```

-   input file is the CSV output from extract_users_from_csvs.py
-   output file will be a CSV, same as the input, but augmented with classification scores
-   the neural net model is the path to the _is_good_or_bad_nnet.dat_ file
-   the ngram model is the path to the _is_good_or_bad_user_desc_ngram_class.dat_ file

### script: split_training_data.py

Splits training data into training and test data. Paths are configured within the script

### script: train_ngram_classifier.py

Trains the ngram classifier from training data.

```
Usage: train_ngram_classifier.py -i (input_file) -o (output_file)
```

-   input file is the training data CSV with user_profile_description and a class_value (bot or good)
-   output file is the .dat file with the trainined ngram model

### script: train_nnet.py

Trains and tests the neural network.

```
Usage: python train_nnet.py -i {input_csv_file}
                            -m {ngram_classifier_model_file}
                            -n {number_training_rounds}
                            -t {testing_file_csv_path}
                            -o {output_model_file}
```

-   input file is the training data CSV with user_profile_description and a class_value (bot or good)
-   ngram_model is the .dat file from the trained ngram classifier
-   number of rounds is the number of rounds to train
-   test file is the test CSV file used to gather accuracy testing
-   output file is the .dat files with the trained neural network model

## License

This software is licensed under the MIT license (see the [LICENSE](./LICENSE) file).

By using this code, you assume all responsibility for any damages, additional charges, and all issues.

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