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Identifying Radical Social Media Posts using Machine Learning

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Radicalization (like cyberterrorism) is one of the major concerns to all governments and law enforcement agencies to provide safety and security to their citizens. A lot of radical groups, extremists and insurgent organizations use social media platforms such as Facebook, Twitter, Reddit, YouTube etc. to post their ideology and propagate their message to their followers. Manual detection of these posts is nearly an impossible task. We propose an automated system for extracting data from Twitter employing investigative data mining technique using the hashtags used in the posts. The system preprocesses the data to clean it by tokenizing, stemming and lemmatization. Data is classified as radical or non­radical using supervised machine learning classification techniques (Naive Bayes, SVM, AdaBoost and Random Forest) with varying parameters. The idea is to classify posts by identifying the linguistic structure, their stylometry and detecting a time based pattern.
Abstract—Radicalization (like cyberterrorism) is one of the
major concerns to all governments and law enforcement agencies
to provide safety and security to their citizens. A lot of radical
groups, extremists and insurgent organizations use social media
platforms such as Facebook, Twitter, Reddit, YouTube etc. to post
their ideology and propagate their message to their followers.
Manual detection of these posts is nearly an impossible task. We
propose an automated system for extracting data from Twitter
employing investigative data mining technique using the hashtags
used in the posts. The system preprocesses the data to clean it by
tokenizing, stemming and lemmatization. Data is classified as
radical or nonradical using supervised machine learning
classification techniques (Naive Bayes, SVM, AdaBoost and
Random Forest) with varying parameters. The idea is to classify
posts by identifying the linguistic structure, their stylometry and
Keywords—Radica lization; Cyberterrorism; Ma chine Learn ing;
DataMining;Shor ttextclassification
Since the late 1980s, the World Wide Web has become a
highly powerful means of communication worldwide,
reaching an evergrowing audience. According to the FBI,
international radicalization has following characteristics: (1) it
involves violent acts harming other human life that can violate    
government laws (2) it is intended to intimidate civilian
masses (3) it affects the policy of any government by coercion
(4) it influences the conduct of a government by kidnapping,
mass destruction, or assassination. Radical groups have started
using the Internet to disseminate information that aid their   
causes. The availability of terrorist related material on the
Internet plays an important role in radicalization processes.
Due to this increasing availability of content on social media
websites (such as Twitter, Facebook and Reddit etc.) there is
In the proradica l networks, large amount of information is
carried and commanded by a limited number of individuals
[1]. Most of the radical groups have shifted their focus from
mainstream media to digital and social media to broadcast the
information to new or younger groups or individuals, who can
get the information by searching through hashtags and further
Research Motivation Twitter is the most commonly used  
microblogging website. Due to the low dissemination barrier
on Twitter, anonymity [3]; anybody can follow anyone (unless
the account is a private account). It has also become one of the
biggest platforms of cyberterrorism holding a lot of extremist
users expressing their views through their posts (tweets). More
than a million tweets are being posted on Twitter everyday, so
analysis of each and every tweet cannot be done manually.
Also, there is a maximum 140 character limit (including text    
and hashtags) and noise i.e. online internet slang,
abbreviations, incorrect grammar and spelling errors makes
the automatic classification of these posts quite challenging.
Moreover, Twitter Search REST API [4] only provides the
data for one week. The aim is to automatically identify if a
We broadly classify our research work and experiment into
A. Data extraction
Extraction of tweets is done from Twitter. Attributes
associated to a single tweet are URLs, text, user mentions,
hashtags, media files (image, audio and video), timestamp,
number of retweets, likes etc. and the user information. The
main focus was on text mining as storing media files would,
firstly, need a lot of space and secondly, analysing media files
for radical content would require additional machine learning
techniques. The texts were collected without knowing the
sentiment. For example, when collecting tweets on hashtag
#Syria (which is in the list of top 10 most frequent hashtags),
The tweet is posted by a person associated with
Someone abominates the very idea of radicalization,
showing disbelief and expressing against a
Someone is discussing something general related to
B. Preprocessingofdata
Data preprocessing is a necessary step after data extraction
in order to make the data unique and nonredundant. As tweets
which have been retweeted by other users occurred multiple  
times in the dataset, thus duplicates were removed using the
unique tweet ID from the Twitter API response. After that, the    
text was strictly restricted to English language. But the
problem arose that a lot of the tweets extracted were in other
languages (like Arabic). Many of the Twitter accounts had
their primary language as Arabic (or languages other than
English) and even if they were radical, could not be
considered. As the results are preliminary, so to write a
unicode to work on tweets in different languages is an
extensive task and out of the scope of this experiment. Then,
to clean the tweets’ texts, URLs were removed for the smooth
processing of data and to reduce overhead. Stopwords were
C. Classificationoftweetsusingmachinelea rningtechniques
Supervised machine learning classification is generally
employed for processing a large quantity of data which cannot
be done manually. Various algorithms can be implemented on
these elucidations to make the classification of tweets as
radical or not. A set of training data containing tweets which
were 100% radical and tweets which were 100% nonradical.
A feature vector for each tweet was generated on the basis of a
unique featureset. The feature vector contained the value as 1
or TRUE if that particular feature was present in the tweet, and
0 or FALSE if feature was absent. Using this training data, a
system is made to learn using classification algorithms: (1)
SVM (Support Vector Machine), (2) Naive Bayes, (3) Adaboost
Classification and (4) Random Forest Classification using
Machine learning is the most common approach for
classification, regression and clustering. Classification
involves identification among various categories a particular
object belongs to. Regression predicts a continuousvalued
attribute which is associated with an object. Cluster ing means
A lot of research in clustering was done in [6], a topic
entity relationship graph was made. The most discussed topics
were the central nodes. All the users who were talking about
it, or have tweeted earlier were linked to that central hub in the
form of a star topology using kmeans clustering. A burst rate
was also taken into consideration i.e. if an account was linked
to that topic in the past, now not posting anything or not in
context with the topic was not considered. In [1] [7], a list
consisting of 66 Twitter accounts which were identified as
radical but were yet blocked by Twitter and the tweets from
these accounts were taken as the training data. The system was
made to learn accordingly and accuracies of each classifier
(SVM, Naive Bayes and AdaBoost) were calculated. The
problem with the list of Twitter accounts was, firstly, majority
of tweets were in Arabic language and secondly, most of the
accounts have been suspended by Twitter leaving not a single  
account good for this experiment’s consideration making it
Many researchers are solicitous for depicting the
phenomena of this social problem revolving around extremist
propaganda.They use online data as a proxy to study the
behavior of individuals and groups. In a 2016 study [8], the
authors carried out three forecasting tasks: (1) Detection of
extremist accounts, (2) To reckon normal user to adopt
extremist content, (3) Predicting whether a normal user will
retaliate contacts generated by the extremist account. Various
machine learning tools are used to create a framework that
generate features of multiple dimensions including network
statistics, user metadata and temporal pattern of activity. Two
scenarios were taken into account for this forecasting process:
a time independent, post hoc prediction task on collected data,
and a realtime simulated prediction task. Further concluded
by determining the emanating signals that provide a thorough
A few researches worked on alternative data sources.
Twitter data was used as a source archive [9], some studies
were based on Arabic tweets and classified these as proISIS
and antiISIS. Provided with the Arab Twitter users (called
tweeps in their research), the account history of users were
known, a model was developed to examine the interest of both
groups (proISIS and a ntiISIS) before and after they started
opposing or supporting ISIS. Trends of tweets or any literature
were analysed to find out the motivations that made people to
follow ISIS online (not considering whether they were willing
to join ISIS or not), identifying ascertained injustices known
as trigger s. Their analysis was divided into two parts: (1)
After collection of data, global trends were determined. This
provided an insight into external affairs such as ‘videos of
beheadings’ or ‘sites of terrorists camps’ (2) Individual
Historic Analysis for the patterns of user history before
supporting ISIS. Limitations for [9], were: their data was
biased to less openly hateful and less offensive users.
Something less vivid than suspension of accounts that users    
themselves can delete their tweets. If an ISIS supporter tweets
about Coldplay, now he can go back in time and delete his
tweet. But expecting this kind of behaviour was limited.     
Another restriction was that Twitter only provides with 3200
tweets for a user, so individual historical analysis would not
provide good results as complete history of a user is not    
Along the same trend, interesting examples proposed by
various researchers [10]  [13] on different machine learning
strategies aimed at detecting hate promotion, extremist support
and cyber recruitment on various social media platforms like
Tumblr, Twitter and YouTube [10]. Their training data was
obtained by semisupervised learning methods and their
framework mainly constituted features of content and
metadata. Their research was divided into many stages
primarily consists of six steps: data extraction, creation of
training data, preprocessing of data, feature set creation and
extraction, data classification and evaluating performance. In
first stage, all the tweets that were in English language were    
extracted and combined to form a single unique dataset.
Second stage consisted of the creation of training data based
on hashtags, as hashtags are the best indicator of the sentiment
of tweets. Tweets were labelled manually and recursively
extended to find new hashtags based on some seed hashtags.
Stage three involves data preprocessing to remove the
hashtags and @username. Stage four includes extraction of
different features on the basis of data extracted. In stage five,
on the basis of two independent classifiers, tweets were
classified as supporting ISIS or not. To complete the process,
the last stage involves creation of confusion matrix to judge
the accuracy of above classifiers. The problem with this
approach was that only the hashtags were considered for the
classification parameters without considering the text or any
other linguistic structure of the tweets since there are a lot of    
tweets without any hashtags and there are many tweets with
Twitter Search REST API [4] was used for extracting the
public tweets for the hashtags which are associated with the
radical groups. A few seed hashtags (#ISIS, #IslamicState)
were selected manually and all the tweets for those hashtags
were extracted. The API only provides the data for 7 days so
the process of extraction was repeated for 4 weeks, giving the
data for roughly 1 month (mid February 2017 to mid March
2017). For the tweets extracted, the frequencies of all the
hashtags were calculated and recursively the most frequent
and unique hashtag was used as the new search query. This
process was repeated for 18 more hashtags (apart from seed
hashtags) giving around 57,698 tweets of which 48,644 tweets    
were unique. The tweets which didn’t have primary language
as English were not considered regardless of their content.
This dataset is called TTFEATURE [19]. After extracting
This data was used to extract the linguistic features and
most popular hashtags. For extraction of these features, a tool
called NLTK (Natur al Langua ge ToolKit) [14] was used. It is
an opensource suite of various libraries for statistical Natural
Language Processing (NLP) for English. It is written in
The texts of these tweets were cleaned by removing the
URLs, user mentions (@), hashtags (#) and the term ‘RT      
(Retweet). The clean text was, then, tokenized using
nltk.tokenize package in the NLTK library giving us the POS
(PartofSpeech) tags for every word. Only the words with
noun POS tags (NN, NNS, NNP, NNPS) [15] were considered.
Many of the words had same meaning but occurred in
different form like jihad, jihadi, jihadist and jihadology. The
derivationally related forms and inflectional forms of a word
were reduced to a common base word (“jihad” in this
#Taliba n
#Wa hhabism
For all the noun words from tweets were stemmed and
lemmatized using nltk.stem.wordnet (WordNet stemmer)
package of NLTK library. The frequency for the base word
was calculated in a Python dictionary. For the abbreviations
like “Islamic State” and “IS” both represent the same thing but
were considered as separate entities during the experiment. To
consider only English noun words, a spell checker tool was
used called PyEnchant. PyEnchant [16] is a Python library for
spell checking. With correct parameters, it helps in identifying
Test data for experiment was extracted using a few of the
most popular hashtags (#IslamicState, #ISIS, #AlQaeda ,
#Wahha bism, #Taliban and #Daesh) as seed hashtags. All the
tweets with at least one of the seed hashtags were extracted
using the Twitter API for one week. 10,282 unique tweets
were extracted (TTTRAINPRO [19]). A random hashtag
(which was safely assumed to be not about any radical
discussion) was taken (#IPL). 15,200 unique tweets were
extracted from this (TTTRAINCON [19]). The dataset of
The tweets in TTTRAIN dataset which were extracted
from the seed hashtags were random in nature as they could be
talking about these radical groups in a supportive way or
opposing them. In order to classify them as radical or      
notradical, many of the tweets were manually classified and
the remainder of the tweets were classified on the basis of the
occurrence of other hashtags. The tweets extracted from the
random hashtags were all safely considered to be not radical in    
nature. The tweets were then cleaned in a similar way as the
tweets in TTFEATURE. Each of them was converted to a   
feature vector of size 613 in length having boolean values.
They were assigned a value corresponding to their manual or
assumed classification as TRUE for radical tweets and FALSE
In the experiment, a total of 613 features were considered
A. StylometricFeatures(SF)
There were a total of 582 stylometric features (Table  II).  
We used the frequency of all English noun words occurring in
the tweets in TTFEATURE dataset after cleaning the tweets
The most frequent 443 words were used as the features.
The 10 most frequent words from these 443 were,  isis” ,
monitor”, “ imam”, “ thanks” , “ world” , “ woman” , “ group” ,
saudi” ,, “ attack”, “ mischief”, “ fighter” , “ car” . Among the
most frequent words used, we can notice the words that are
related to radical activities (isis”, “attack” ), words that can
or cannot relate to radical activities ( world” , “woman” ,
car) and also the word imam. The word imam refers to the
person who leads prayers in a mosque. It is a word originated
The 139 most frequent hashtags were used for the
experiment. The 10 most frequent hashtags were, #ISIS,
#IslamicState, #Taliba n, #IS, #Wahhabism, #AlQa eda,
#SaudiAra bia, #ISIL, #Syria , #Daesh. It can be observed that
all of the most frequent hashtags are related to radical groups
and their activities. #ISIS, #IslamicState, #IS, #ISIL, #Daesh
all signify the Sunni militant group. #AlQaeda and #Taliban
also are militant groups, whereas #SaudiArabia and #Syria are
B. TimePatternFeatures(TF)
The date and time at which the tweet was posted for
recognizing a pattern was used. The day of the week and the
hour of day were used since other attributes like month were
not relevant in our experiment since the data from 4 weeks.
In total 31 time pattern features were used, 24 for hours and 7   
A tool called scikitlearn was used for various
classifications. It is an open source Python library for machine
learning built using NumPy, SciPy, and matplotlib libraries. A
dataset with 5,297 tweets was extracted using the same
extraction method as TTFEATURE dataset using a seed
hashtag (#GameOfThrones). This dataset was called TTTEST
[19] and the ratio between training data and testing was
approximately 1:5. Different classification algorithms used
were: Naive Bayes, SVM (Support Vector Machines), 
AdaBoost and Random Forest Classification. SVM was tested
over different values of its “kernel” parameter to get the
It can be seen that AdaBoost Classifier works slightly
better than Random F orest Cla ssifier. Naive Bayes provides
the worst results out of all the classifiers for this experiment.
For SVM, choosing the correct kernel improves the results.
Radial basis function (Gaussian) (RBF) kernel performs better
TTTRAINPRO dataset was analysed for understanding
the time based pattern of when people generally tend to post
proradical tweets. It was observed that the maximum radical
tweets were tweeted between 1:00 AM GMT and 2:00 AM
GMT while the minimum radical tweets were tweeted between
10:00 PM GMT and 11:00 PM GMT. The results are
In this work, supervised machine learning was used to
identify radical social media posts. The major problem with      
this classification was manually labelling the test data as
radical or not radical. The dataset extracted was dependent on
the seed hashtags which were selected initially. In future more
independence to dataset should be there. The texts, images and
videos issued by various radical and extremists groups which
are collected by intelligence agencies can also be used to
improve the performance of the system. Moreover, this
classification should be seen as a support to the manual
checking of the tweets since the accuracy of no classifier is
absolute 100% and there is a chance of wrong tweets being   
identified as radical. This is due to the dynamic nature of the    
tweets and the noise in tweets due to abbreviations, Internet
The untagged datasets used in the experiment
(TTFEATURE, TTTRAIN and TTTEST) have been made
public for future use which can be found on repository hosting
website, GitHub [19]. The data is directly downloaded from
The obtained results are from a limited dataset, that too
only from one social media website, Twitter. In future the
experiment can be extended to a diverse and large dataset from
multiple websites (like Facebook, YouTube, Reddit, etc.) and
platforms. The URLs in the tweets which were ignored here
can also be analysed to identify the nature of tweets. Also,
considering nonEnglish languages tweets (like Arabic) can
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[19] RadicalizationTwitterDataset,(,2017
... Two datasets were released in 2016 and contain 17,000 and 122,000 messages related to ISIS activities, respectively [13] [14]. Another was published by Gupta et al. [15] in 2017 with 48,000 messages related to several terrorist groups, including Al Qaeda and the Taliban. All of these datasets are in the English language and, as such, cannot be used as training tools for other languages, which limits their practical utility. ...
... These two datasets have been used in many studies [1], [17], [18], [19], [20], [21], and their availability has enabled the development of extremism detection techniques. Gupta et al. [15] released a data repository on GitHub [22] to assist in the identification of radical social media posts using machine learning. The authors used the Twitter Search REST API to extract public tweets that were posted between mid-February 2017 and mid-March 2017. ...
... As shown in Table 1, the most commonly implemented algorithms were support vector machine (SVM), random forest (RF), and long short-term memory (LSTM). In several studies, including [28], [10], and [29], the accuracy of SVM exceeded 90%, and in [25], [15], and [30], the accuracy of the RF algorithm was higher than that of the SVM. Recently, deep learning techniques, particularly convolutional neural networks (CNN), originally developed in [31], and recurrent neural networks (RNN), proposed in the late 80s [32], [33], have yielded notable results. ...
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The dissemination of extremist ideas and causes online has intensified over the last decade. Extremist organizations use social media to gain publicity and new recruits, often with little interference from network providers. New techniques are being developed to identify extremist content, ensuring it can be promptly removed and its authors blocked from network access. However, most techniques are only compatible with the English language, despite the fact that extremist propaganda is frequently shared in other languages, including Arabic. Since the most effective methods for automated linguistic analysis use deep learning and require large, high-quality datasets, creating specialised data samples containing examples of extremist communication is an essential step toward a practical solution. In this paper, we present a dataset compiled for this purpose and discuss the classification methods that can be used for extremism detection. The manually annotated Arabic Twitter dataset consists of 89,816 tweets published between 2011 and 2021. Using guidelines, three expert annotators labelled the tweets as extremist or non-extremist. Exploratory data analysis was performed to understand the dataset’s features. Classification algorithms were used with the dataset, including logistic regression, support vector machine, multinominal naïve Bayes, random forest, and BERT. Among the traditional machine learning models, support vector machine with term frequency-inverse document frequency features achieved the highest accuracy (0.9729). However, BERT outperformed the traditional models with an accuracy of 0.9749. This dataset is expected to enhance the accuracy of Arabic online extremism classification in future research, and so we have made it publicly available.
... Kamath et al. [18] presented an analysis of various machine learning and deep learning based approaches for sentiment classification such as decision trees, logistic regression, support vector machine, Naïve Bayes and Convolutional Neural Networks. P.Gupta et al. [22] presented the approach proposed a SVM and RF algorithm for sentiment analysis .on twitter data. ...
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Conference Paper
The increasing complexity and emergence of Web 2.0 applications have paved way for threats arising out of the use of social networks by cyber extremists (Radical groups). Radicalization (also called cyber extremism and cyber hate propaganda) is a growing concern to the society and also of great pertinence to governments & law enforcement agencies all across the world. Further, the dynamism of these groups adds another level of complexity in the domain, as with time, one may witness a change in members of the group and hence has motivated many researchers towards this field. This proposal presents an investigative data mining approach for detecting the dynamic behavior of these radical groups in online social networks by textual analysis of the messages posted by the members of these groups along with the application of techniques used in social network analysis. Some of the preliminary results obtained through partial implementation of the approach are also discussed.
Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps.
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