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How They Tweet? An Insightful Analysis of Twitter Handles of Saudi
Arabia
Mohammad Muzammil Khan∗1, Shahab Saquib Sohail1, and Darakhshan Ishrat2
1Dept. of CSE, SEST, Jamia Hamdard (India)
2Dept. of IS, SHSS, Jamia Hamdard (India)
Abstract
The emergence of social network site has attracted many
users across the world to share their feeling, news, achieve-
ments and personal thoughts over several platforms. The
recent crisis due to worldwide lockdown amid COVID 19
has shown how these online social platforms have grown
stronger and turned up as the major source of connection
among people when there is social distancing everywhere.
Therefore, we have surveyed Twitter users and their
mannerism with respect to languages, frequency of tweets,
the region of belonging, etc. The above observations have
been considered especially with respect to Saudi Arabia.
An insightful analysis of the tweets and twitter handles of
the kingdom has been presented. The results show some
interesting facts that are envisaged to lay a platform for
further research in the field of social, political and data
sciences related to the Middle East.
Keywords. Saudi Arabia Middle East Twitter So-
cial studies Behavior..
1 Introduction
Twitter is a social networking and micro-blogging platform.
The service gained rapid success and popularity as it pro-
vided a unique way to interact with the Internet termed
as “tweets”. Tweets can be a variety of services provided
on the platform which include text, photos, videos, polls,
etc. The service was launched in the year 2006 and after
just 6 years, in 2012, the micro-blogging site had gained
140 million active users with over 300 million tweets a day
[1]. The platform initially restricted a tweet with a limit
of 140 characters. However, in 2017 Twitter doubled the
limit for non-Asian languages to 280 characters [2]. Twit-
ter provides many ways to categorize and tag tweets. For
combining similar tweets based on content-type or combin-
ing similar tweets based on a ticker symbol for stocks and
companies, “hashtags”, can be used and usually known as
trend/trending over Twitter.
Twitter generates an enormous amount of data that can
∗Contact: mmkhan.sch@jamiahamdard.ac.in
be accessed using the official Application Program Interface
(API). The API requires an authentication key to access the
data. However, there are limitations for each type of API.
These limitations manage the number of times a request
can be made, the amount of data a single user can access,
and how much of the Twittersphere is accessible to a user.
These limitations are here to protect data usage and prevent
personalized and targeted approach for an individual or a
group of individuals.
We aim to collect the data as much as possible respecting
the limits and restrictions in place and perform an analysis
of the tweets to find some useful analytical data which can
be used to analyze the behaviour of users tweeting in a
certain region. We shall collect and analyze the tweets from
the perspective of the Kingdom of Saudi Arabia (KSA).
2 Background
2.1 Related work
In general, researchers have been studying and exploring
the social network for quite a while now. Many of the
researchers have applied their studies and found influence
patterns [3]. Researchers, regarding the influencers, have
found that user accounts of news media are better spread-
ers of contents and that the celebrities use Twitter to make
conversations [4]. Researchers in [5] focused a study for the
United States of America of demographic data using the
Twitter data from [3] and have been able to identify a lo-
cation, gender, and race/ethnicity only from the publicly
accessible data. Researchers have been able to use the data
generated from online social networking sites for predicting
many things that seem random and/or can’t be humanly
predicted. These predictions include the stock market [6,
7], book sales [8], movie-ticket sales [9, 10], product sales
[11], infections, diseases, and consumer-spending [12]. Au-
thors have also incorporated Twitter data for healthcare
purpose and disease prediction [13, 14].
There have been few works with respect to Saudi Ara-
bia and Arabic (language) tweets. In 2011, Al-Khalifa [15]
performed a social network analysis of the Twittersphere
to understand the social structure of citizens and their in-
1
arXiv:2105.10736v1 [cs.SI] 22 May 2021
teractions as discussions of issues regarding politics. Since
then Arabic dialects are receiving a lot of attention from
researchers. With the use of natural language processing,
many researchers are now able to investigate even more
deeply in an unprecedented manner. In [16], Al-Twairesh
et al. have conducted a study of the Modern Standard Ara-
bic (MSA) usage by the Arabic-speaking users of Twitter.
Just like the predictions made in [6 – 12], researchers have
also been able to predict the Saudi Stock Market Index [17].
Authors have also been able to detect sarcasm through anal-
ysis of the Arabic tweets [18]. In 2018, researchers targeted
the Arabic-tweeting community for profiling to help iden-
tify and extract attributes of a Twitter user using machine
learning-based classification of topics from Tweets which
achieves a 90% accuracy [19]. Sentiment analysis is also be-
ing used in research to provide a better understanding of
using text mining [20]. An emotion and mood visualizer
called “Saudi Mood” was proposed in [21] to continuously
monitor the Arabic Twitter to detect dominant emotion
in the Kingdom of Saudi Arabia using real-time sentiment
analysis. Another sentiment analysis [22] was used to get an
insight into the different dialects used in the Arabic tweets
using sentiment analysis of the Arabic tweets.
2.2 Key issues addressed in this research
As of January 2020 [23], there are 14.35 million Twitter
users from Saudi Arabia, with a rank of 4, followed by
59.35, 45.75, and 16.7 million users from the United States,
Japan, and the United Kingdom respectively. As the num-
ber of users is higher, we can infer that the number of tweets
from the Kingdom is higher as well. We have investigated
whether Saudi Arabia falls in the top 10 most tweeting coun-
tries or not?
In addition, Arabic is the lingua franca of the Arab world.
Being the 6th most-spoken language worldwide [24], we have
critically reconnoitred the ranking of Arabic among the top
10 languages used on Twitter. Regardless of the dialects
spoken, Egypt is the most populous Arabic-speaking coun-
try [25]. However, a lot of factors come into play like so-
cial awareness of the platform, social networking norm in
the country, etc. when we ponder over who tweets more.
According to [26], Facebook is the most popular social me-
dia platform used by the Egyptian public and Twitter is
the 3rd. According to a report [27], Facebook is also the
most favoured in Saudi Arabia, however, Twitter comes
2nd. Naturally, we have inspected that which country will
have more Arabic-tweets, Saudi Arabia, or any other coun-
try like Egypt?
A study conducted in 2016 [28] shows that the best time
to get most interactions for a tweet is around noon, from 12
PM to 1 PM. This study was conducted on a high volume
of tweets. However, they did not include or mention Saudi
Arabia in the research explicitly. We have surveyed the
Twitter data to find out the period in which most frequent
tweets are tweeted concerning Saudi Arabia.
3 Data crawling and information
fetching
To maximize efficiency and productivity, we have divided
the work into different yet integrated modules. The whole
set consisted of 4 modules which performed tasks by taking
the input of the previous module’s data and generate output
to be used as input for the next module. The modules are
– Crawler, Processor, Analyzer, and Pruner.
A customized crawler was created which accessed the
tweets using the official API in real-time and stored the data
synchronously in a well-defined format. We ran the crawler
for around 23 continuous days with API rate-limiting of 450
requests per 15 minutes and got over 86.79 million tweets.
The Crawler was programmed to save only the tweets in
which – 1. the user has entered their location, and 2. lan-
guage was detected by Twitter. After trimming the re-
sults, we had a dataset consisting of 58.15 million tweets.
We also created a custom processor to process in whichever
way we wanted. In the Processor, we programmed two sub-
modules, i.e. a sub-module to check and verify the integrity
of dataset and a sub-module to detect and extract countries
and cities from a plain text which in this case is the loca-
tion field self-reported by the user. We ran the Processor
twice. In the first time, we performed a check on dupli-
cation and the false negatives of location detection. After
the first round of processing, we found that there were 8.2
million duplicate tweets and 2.36 million distinct entries for
unknown/undetected locations from 21.69 million tweets.
Then, we manually evaluated the false negatives and ad-
justed the algorithm with a modification of 0.37 million en-
tries. While evaluating, we found that people were using a
wide range of spellings and native languages for their loca-
tions. This process also confirms that the dataset format is
error-free as we were able to process the whole dataset of
crawled tweets.
After deduplication and adjustments, we ran the Proces-
sor again, we had a data-set of 49.99 million tweets and were
able to detect countries of 68.49% of the data-set. Then the
analyzer came into the picture. The analyzer analyzed each
tweet depending upon the inputs provided to categorize the
data. We used a combination of regular expressions, map-
ping, and hardcoded commands to sort out the data and
to output the analysis in comma-separated-value format.
This format can be imported into any analysis tool for fur-
ther study. We programmed the Analyzer to show some
of the preliminary analysis after the completion of execu-
tion. The preliminary analysis showed that there were –
12.85 million users from which 7.24 million posted tweets
and 8.67 retweeted with 3.07 million users doing both, the
dataset had 33.84 million words, 64 languages, and 237
countries. Finally, the Pruner analyzed the data which was
more than 16 GB and was too big to load every time to
look upon. So, the Pruner produced the output, i.e. the
sorted and trimmed data, which was relatively small and
much portable.
2
Table 1: Languages used for tweeting in KSA
Language Tweets Language Tweets Language Tweets Language Tweets
Arabic 61390 Korean 75 German 19 Norwegian, Malayalam 4
English 27161 Hindi 66 Romanian 14 Thai, Swedish, Danish 2
Spanish 464 Persian 59 Vietnamese 13 Bangla, Kannada,
Catalan 407 Italian 55 Welsh 11 Polish, Telugu,
Urdu 403 Japan 53 Czech 10 Sindhi, Hebrew, 1
Tagalog 396 Haitian 38 Tamil 9 Icelandic, Lithuanian
French 267 Turkish 31 Finnish, Chinese 8
Portuguese 226 Estonian 25 Dutch 7
Indonesian 161 Pashto 20 Hungarian 5
Table 2: Arabic-tweeting countries
Rank ISO 3166-2 Country Tweets
- - Unknown countries 98707
1 SA Saudi Arabia 61390
2 EG Egypt 12415
3 KW Kuwait 9306
4 AE United Arab Emirates 4995
5 US United States 3748
6 IQ Iraq 2374
7 BH Bahrain 1711
8 OM Oman 1609
9 QA Qatar 1592
10 GB United Kingdom 1274
- - Others 10122
Figure 1: Asian countries and number of tweets recorded
during the period of the experiment
4 Results and Discussion
4.1 Frequency of the Tweets
Our analysis of the crawled tweets has shown that among
the 237 countries, the United States of America was on the
top in the frequency of tweet creation, retweeting, and both
combined, followed by Brazil in each category. The King-
dom of Saudi Arabia ranked 27th in tweet creation, 37th in
retweeting, and 35th in the overall frequency of tweets. On
a continental level, The Kingdom of Saudi Arabia is ranked
at 11th position with India having ranked first as shown in
figure 1.
Figure 2: Time duration and total tweeted Tweets during
the period
4.2 Most frequent language in tweets
Twitter provides with language detection of tweets within
the API. At the time of crawling, we selected and saved
only the tweets in which the API was able to detect the
language. Our dataset had a total of 64 languages, among
which English was the most popular language with 29.35
million tweets, followed by Spanish with 8.62 million and
Portuguese at 6.31 million, and the least was, with mere 5
tweets, Laotian – a native language of the people of Laos.
Here, we infer that Arabic which is the official language of
the Kingdom, ranked at 8th position with over 0.21 million
tweets or amounting to 0.42% of the whole dataset. More-
over, as shown in table 1, Arabic is found to be the most
popular language in the KSA with 61,390 tweets, amounting
to 67.15% of tweets originating from the country. Followed
by English and Spanish with 27,161 and 464 tweets respec-
tively and Lithuanian, Icelandic, Hebrew, Sindhi, Telugu,
Polish, Kannada, and Bangla languages having only 1 tweet
each.
Despite being the most popular language among the user
base of Twitter residing in the Kingdom, Arabic tweets from
the Kingdom only amount to 29.34% of the whole dataset
and 47.17% of tweets come from unidentified locations from
which a percentage could very well be residing in the KSA
itself. The KSA is followed by Egypt, Kuwait, the United
Arab Emirates, the United States, and Iraq with 5.93%,
4.45%, 2.39%, 1.79%, and 1.13% of the Arabic tweets re-
spectively (table 2).
3
4.3 Usage during the day
The Twitter API returned each tweet with the timestamp of
creation in the Coordinated Universal Time (UTC) format.
We stored this as well and then converted the format into
Arabian Standard Time (AST). We saw that the Saudis
are the least active at dawn, specifically 0400 hours AST
or 0100 UTC, slowly picking pace and achieving peak us-
age in the afternoon, specifically 1300 hours AST or 1000
hours UTC. Afterwards, usage declines until 1600 hours
AST (1300 UTC) to 1800 hours AST (1500 UTC) (figure
2).
This finding corroborates with the study [28] and suggests
that the best time to get most interactions from a tweet is
at noon, specifically around 1300 AST, as most of the users
are active around that time.
5 Conclusion
In this paper, we crawled around 86 million tweets dur-
ing 23 days and then processed and analyzed the data con-
cerning the trends and behaviour in Saudi Arabia and the
Arabic language over Twitter. Primarily we are focused
on exploring whether Saudi Arabia is in the top 10 tweet-
ing countries as the number of Twitter users in the KSA
is relatively higher, hence more users should result in more
tweets. We have found that Saudi Arabia is placed in the
11th position as far as the number of tweets is concerned.
Since Arabic is among the leading languages spoken world-
wide, therefore, we have also identified how users at Twitter
across the Globe behave when language is concerned and it
is found that Arabic remains one of the top languages with
respect to its frequency over Twitter. In addition, it has
been observed that the Kingdom of Saudi Arabia has the
most Arabic-tweets when comparing to Arabic tweets from
any other country worldwide. The earlier study suggests
that the most active time for users is at noon. We have in-
vestigated and found the most frequent tweeting time comes
to be 12 pm – 1 pm across the respective time zones of the
countries concerned. Further, in the future, we would like to
analyze this area by performing deep analysis on the tweets
themselves and their impact on the Arab culture. We can
perform sentiment analysis on tweets to further classify and
extract features. This study is believed to establish a base
of future Twitter research not only for Arab-centric but also
to other regions and other aspects.
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