BotBoosted

An App that Measures and Explores the Fabrication of a Queried Twitter Topic

Abstract

Content is fraudulent or fabricated if it is created by a non-human/non-genuine account. These accounts include spam-bots, fake-accounts, spammers, and otherwise --> with the intention of "inflating" or "fabricating" the level of popularity of a certain topic, so as to mislead people.

Topics whose popularity are inflated can be a Political Candidate, a brand, a celebrity, or an ideology. For these examples, someone will have something to gain - at the expense of the truth.

The goal of this project/prototype is to give people a tool to understand how much of a topic one searches on Twitter is genuine or otherwise, and what the real and fabricated conversations are about.

This prototype has three main parts:

1. A classifier to determine whether a tweet's user account is genuine/not
2. A topic modeler to break down the conversation into understandable "chunks" - for the easier interpretation of the user, and so that the user can see how much of each subtopic within the query is made up of real and fake conversations
3. A corpus summarizer that picks out the most important real and fake tweets so that the user can easily understand what is going on

DISCLAIMER: this app uses Twitter's FREE Rest API in searching for tweets. The Rest API is able to give users free access to a sample of tweets that are NOT statistically significant. As a result, no inferences can be made about the percentage of fraudulence/fabrication of a queried topic through this prototype. This prototype simply demonstrates how this might work SHOULD one have access to a statistically significant sample of data.

Main Contributions:

Aside from the app prototype, this project makes two major contributions

1. A lightweight classifier that only uses class A features in determining whether an account is real or fake, while being as accurate as classification models that required class A and class B features

2. A heuristic lightweight model for determining the number of topics in a given corpus named Incremental Pareto NMF (IPNMF).

Major Parts

The backbone of this app is made in python, whose modules are as follows:

1. load_train_data.py

   • These are a series of helper functions that extract the raw information from the training data csv files that were put together by Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi for their paper "Fame for sale: efficient detection of fake Twitter followers." http://mib.projects.iit.cnr.it/dataset.html is the link to their data. The main orientation of this module is to compile the different csv files that this research team put together into one single and properly labeled csv.

2. process_loaded_data.py

   • This module takes functions from load_train_data in order to load information, process the different features into the specific items needed by the classifier, and then combines the user information with the tweet information into a single csv file named "training_df.csv"

3. lightweight_classifier.py

   • Previous research in the area of classifying twitter users as real or fake has done so by using class A (lightweight) and class B (costlier) features. Lightweight features include everything that you can get from a single tweet (total tweets, follower, likes, account creation date) as these are embedded in the json object that one can get when downloading a tweet via twitter's API. Costlier features include a user's tweet history, meaning the tweets themselves.

     The contribution to the research community of this lightweight classifier is a classification method that relies solely on class A features. The approach is as follows: a) create features from user's account history (total likes, total tweets, total followers, total friends, etc) b) create features that express relative volume (total likes divided by total number of followers, total tweets divided by total number of friends, etc) as it was observed that some accounts have hundreds and thousands of tweets but very few people in their network c) create features that express behavior rate (total likes per day, total tweets per day, total likes per friends per day) as the account creation date is available in the json object and it was observed that fake accounts do "machine gun" tweeting where they tweet very frequently in a small period of time. These set of features was added in order to also make the model less naive to new users

     No features took the content or the words of the tweet into account (i.e. NLP based prediction) as the premise is that a human is always behind the message being artificially propagated. The behavior captured by the tweet was taken into account by looking at hashtag usage, mentions, whether the tweet was favorited by another person, etc.

     The classification model is a random forest ensemble made up of three random forest models.

     1. Random Forest 1 (RF1) takes in account history features and relative volume features
     2. Random Forest 2 (RF2) takes in behavior rate features that look at account history features per day and relative volume features per day
     3. Random Forest 3 (RF3) takes in the predicted probabilities of Random Forest 1 and Random Forest 2, along with all of these models features, and then makes the final prediction.

     The final Random Forest is able to balance out the work of the previous ones by understanding the user patterns along the two major facets: account history and account behavior rate.

     The ten fold cross validated accuracy of RF1 is 97%, RF2 has 95%, and RF3 has 98%. Previous research using this dataset achieved these kinds of scores as well. However, they did so with class A and class B features. The contribution of this work is that this kind of performance was attained using only class A features.

4. lightweight_predictor.py

   • The objective of this module is to load the random forest ensemble in order to create a dataframe that has the major information about the predicted tweets. This information includes the user's screen_name, the tweet itself, and whether the tweet is real or fake

5. tweet_text_processor.py

   • These are a series of functions that, at a high level, do the following things:

     a) Tokenize Tweets in a Twitter specific way (convert links into "url"), remove usernames, remove hashtags, correct the spelling of words (i.e. "goooooooooood" --> "good") for normalization purposes, convert emoticons into words (i.e. :) --> "happy"), remove punctuation, remove stopwords b) Vectorize the Tweet-Tokenized documents into WordCounts or TFIDF for the extraction of topics via IPNMF c) Soft cluster each document with their corresponding topic number and then compute for word importance using a random forest's feature importance where the features are the tweet's tfidf values and the labels are the soft clustered topic labels for each tweet d) Determine most important words/sentences/tweets by multiplying the tf-idf with the feature importance, as a means of determining the "exemplary tweets" that make up that topic e) Create a stacked barplot that shows the distribution of the real and fake tweets within the different subtopics of the tweet corpus, and a percentage stacked barplot that shows how much of each subtopic is real and fake

6. paretonmf.py

   • This is the class that dynamically determines a heuristic count of the number of topics inside a corpus using Incremental Pareto NMF. The three major parameters to tune for this heuristic include the ff: a) noise_pct - this is the percentage of the total documents that IPNMF keeps track of in the tail of the topic distribution b) start - this is the initial number of topics that IPNMF extracts from the corpus c) step - this is the size of the step, akin to boosting ensemble methods' learning rate, that IPNMF takes in incrementally extracting topics from the corpus

7. tweet_scraper.py

   • This module is responsible for downloading the tweets into a list of json objects that contain the tweet information as well as the basic account history for each user

8. tweet_scrape_processor.py

   • This module is responsible for processing each json object into the different features necessary for the prediction model module so that the random forest ensemble can make predictions on newly downloaded tweets, or tweets store in a mongo database, so long as they are in the form of json objects in a list

9. main.py

   • This module integrates all of the different modules into two main functions:
     • botboosted - this function allows a user to specify a search, and then this will download the tweets, classify them, and visualize them as different subtopics, and summarize them with exemplary tweets
     • botboosted_demonstration - this function replicates the previous function but works on tweets inside a local mongodb rather than tweets that are downloaded through twitter's api

Borrowed modules

1. emoticons.py this module uses regex to identify emoticons in a tweet and convert them into the equivalent word (i.e. happy face is replaced with the string "happy")
2. twokenize.py this module uses regex to identify relevant characters inside a tweet such as the ampersand for mentions or the octothorp for hashtags

These modules were both borrowed from: Aritter. Twitter NLP. (2016). Github repository https://github.com/aritter/twitter_nlp

Deprecated Modules

These modules were used in the initial version of this project:

1. classification_model.py this module has the original feature set used by Azab, A., Idrees, A., Mahmoud, M., Hefny, H. in their paper "Fake Account Detection in Twitter Based on Minimum Weighted Feature set.". This was deprecated because a more efficient method of classifying tweets was developed in the course of this project

2. corpus_explorer.py this module has the initial visualizations that included plotting the different topics on PC1 and PC2, as well as the different tweets on PC1 and PC2, but was deprecated because a more effective visualization was developed that involved stacked barplots

3. evaltestcvbs.py this is a class that analyzes the precision and recall of a classifier across different split percentages in the test set in order to gain a better understanding of the classifier's performance

4. information_gain_ratio this is a set of functions that compute for the information gain raio from Ross Quinlan's C4.5, and was originally intended to be used to determine word importance, but was replaced by the feature importance attribute from sklearn's random forest

5. optimize_model_ensemble.py this module is used to host a set of functions that would work to get the best weightings for the predictors created by the deprecated classification_model.py module, and was removed because of the development of the more lightweight classifier, the random forest ensemble

6. prediction_model.py this module is used to make predictions using the models made by the deprecated classification_model.py module

7. load_test_data.py this module was used to load class A and class B feature data from a mongodb and process them for the predictions that will be made by the deprecated classified model module