Comparative Analysis of the Content of Online Magazines of the Islamic State of Iraq and Syria (ISIS) in Different Languages: Dabiq, Rumiyah, and Konstantiniyye

 Abstract

The Islamic State of Iraq and Syria (ISIS) effectively uses online magazines for propaganda, leveraging advanced internet technologies to disseminate its message in multiple languages. This study investigates ISIS's use of online magazines to promote its self-proclaimed jihad and attract recruits globally. While existing research examines ISIS's multilingual magazines, few studies comprehensively compare them across languages. This study conducts a text analysis of Dabiq and Rumiyah in English and Konstantiniyye in Turkish. The findings reveal that all magazines construct distinct “us” versus “them” identities, focusing on religion and justification. Konstantiniyye, targeting Turkish Muslims, emphasizes stronger religious themes compared to Dabiq and Rumiyah. Overall, these magazines discuss legal systems, jihad, state structure, and social order, advocating for Muslim unification under a single “caliphate.” This analysis sheds light on ISIS's diverse propaganda strategies tailored to different linguistic contexts.

Keywords: Islamic State of Iraq and Syria (ISIS); Dictionary-based Analysis; Dabiq; Rumiyah; Konstantiniyye

LINK:  https://journals.tplondon.com/ipr/article/view/3313

Terrorism-Related Topic In The Language Of Fake News In Indonesia

The discussion on the relations between terrorism and social media mainly focuses on social media's role in spreading terrorism ideology and recruiting a member of the terrorist group. However, social media has also become means to share fake news related to terrorism. Considering the relationship between fake news and terrorism, the primary purpose of this study is to examine the content, language used, and emotion of fake news about terrorism in Indonesia. It also revelas what kind of language is used in fake news to manipulate the public and what emotions it appeals to? We analyzed fake news about terrorism in Indonesia using quantitative text analysis methods, such as sentiment analysis and dictionary-based analysis methods with R statistical software. We created two dictionaries covering religious and violent words, and fake news was examined under these two dictionaries. For reliability, we applied the "Split-half test" to the results and we reached similar results. This study shows that ISIS, Aceh, and terrorist action in Sulawesi are three dominant topics in fake news. Second, the language on fake news is mostly about terrorism and violence to create fear in society. Third, violence and religious language are equal in the language of misleading content. We interpreted the results obtained with the traditional fear of crime theory. We then discussed the significant results of this research and made effort to explain the reasons behind our research results. The study has a limitation because of the small number of fake news. Future studies may overcome this limitation by selecting multiple countries as cases or increasing the data range of fake news. The dictionary-based method we used in this study is relatively new to the literature and provides the opportunity to analyze fake news content effectively. Our results using the Dictionary-based method can provide valuable clues for policymakers in the counter-terrorism field.

Link: https://ejournal.upnvj.ac.id/index.php/JM/article/view/MJIHI%20_01

Examining ISIS's Turkish Sympathizers on Twitter

Abstract

Social media platforms have become a part of people's daily lives. However, developing social media technologies have affected not only people but also terrorist organizations such as the Islamic State of Iraq and Syria (ISIS), and social media has become an essential part of ISIS's strategies. ISIS effectively uses many social media platforms, especially Twitter, for propaganda, recruitment, and creating fear among people. ISIS also actively uses Twitter in Turkey. However, the number of academic studies focusing on the behavior, structure, communication, and relationship networks of ISIS sympathizers in Turkey is limited. Therefore, the main purpose of this study is to reveal the structure, behavior, communication, and relationship networks of ISIS's Turkish sympathizers. Accordingly, this study focuses on the structure, behavior, communication, and relationship networks of ISIS's Turkish sympathizers. I will analyze them within four main analytical steps. The basic characteristics, structures, behaviors, and relationship networks of 2079 Turkish ISIS sympathizers are examined with the R computer program, and many remarkable findings are reached as a result of the analysis.

Keywords: ISIS' Turkish Supporters, Twitter Analysis, Network Analysis, Correlation Analysis, Social Media

LINK: https://dergipark.org.tr/tr/pub/icps/issue/74445/1183919

SentiStrength

 

SentiStrength is a software program that analyzes sentiment. SentiStrength gives sentiment results in three types. First, it gives us the results in binary form. In other words, it gives the sentiment results as positive or negative. Second, it gives it as a trinary. That is, the results are presented as positive, negative or neutral. Finally, it gives sentiment results as a single scale. In other words, sentiment results are given on a scale between -5 and +5. SentiStrength supports many languages such as German, English, Finnish and Turkish and is capable of sentiment analysis of texts written in these languages. The SentiStrength program is completely free.

10108 tweets about online food ordering services are analyzed with the SentiStrength program to explain this software in depth. The main problem we encounter at this stage is that the SentiStrength program only accepts documents with the ".txt" extension. If the text you want to analyze is ".xlsx" extended, you should deal with this issue first. Unfortunately, the file I would to analyse has the ".xlsx" extension. For this reason, I will first convert the section I want to examine in the excel file to .txt format. In this context, delete the sections except the section we want to analyze, and only the column we want to analyze remains in our file. In the next step, we need to remove the duplicates to avoid recounting the same tweets. In Excel, we can remove duplicates using the “Data-> Remove Duplicates” function. The logo you see marked is the logo that belongs to the "remove duplicates" function.

Then save our excel file, which we cleaned from repetitions, as unicode text (.txt). In the next step, open the SentiStrength program and select the "Analyze ALL Texts in File (each line separately)" function in the "Sentiment Strength Analysis" section.

Next, let's select the .txt file we want to analyze. Before the analysis starts, SentiStrength will ask if you want to add a header line to the resulting file, and we need to select "Yes". SentiStrength will ask us which column to use for analysis and we can directly write "1" here.

If this image occurs, it means that our analysis is complete. However, at this stage, the problem we encountered at the beginning reoccurs. The results are saved on our computer with the .txt extension.

It will be useful to copy and paste the results with .txt extension to the excel file so that we can see the results more clearly.

At the last stage, we will have an excel file with 5 columns such as positive, negative and EmotionRationale. When we look at the results, it is seen that there are many -1 and +1. -1 means not negative or neutral. +1 means not positive or neutral. Between 2 and 4 is positive and 5 is extremly positive. On the other hand, between -2 and -4 is negative and -5 is extremely negative.

Finally, we can add another column named "score" next to the negative and positive columns in the excel file and see the general sentiment result. For this process, we need to use the following formula; "=C2+D2". The results we get with this formula will give the overall result of each tweet we analyzed.

 ISIS Religious and Extremist Propaganda on Social Media: Dictionary-Based Study of Twitter

Abstract

The world had faced with many terrorist organizations until 2014. However, after 2014, the world faced with the most complicated terrorist organization. This terrorist organization is ISIS or Islamic State of Iraq and Syria. ISIS has been running a more different propaganda campaign, emphasizing the state-building and welfare schemes run by this organization and these elements make ISIS more complicated. ISIS has been very well integrated into the new technology such as social media and smartphone and ISIS has been using them very effectively. Especially Twitter has become a major component of ISIS social media movement. Twitter was used to spread sensationalistic ISIS photos and videos across the Twitter users. While ISIS spread fear and messages on twitter, at the same time it also gained supporters. However, it is seen that ISIS’ sympathizer uses different jargons in terms of their number of followers in twitter. As a result of my research, I found that users with more followers used a stronger violence jargon on Twitter, while users with fewer followers using a softer and more religious language. Users with less followers were an emphasis on unity and religion, while users with more followers encouraging physical violence such as lone wolf attacks and killing enemy appeared more often on Twitter. Dictionary-based analysis of ISIS' and its sympathizers' tweets were performed. This dictionary-based research creates a typology to explain and categorize tweets from ISIS and its followers. For reliability, "Split-half test" was applied to the results and similar results were reached.

Keywords: ISIS, Twitter, Dictionary-Based Analysis, Propaganda

RELATED LINK: https://smallwarsjournal.com/jrnl/art/isis-religious-and-extremist-propaganda-social-media-dictionary-based-study-twitter

The Opinion of The Taiwanese Media on Pro-Independence and Pro-Unification with People Republic China During Tsai Ing-Wen Era

Abstract: Taiwan-People’s Republic of China (PRC) relations have attracted a lot of attention in recent years. These relationships are at the core of Taiwan's social and political life. The pro-independence and pro-unification social cleavage line affected by Taiwan-PRC relations divides Taiwan's political life into two separate camps. These camps are pan-blue coalition (pro-unification) and the pan-green coalition (pro-independence). In this study, the views of Taiwanese newspapers containing both camps on Taiwan-PRC relations are analyzed quantitatively. The study focuses specifically on the Tsai Ing-Wen period. Within the scope of the study, dictionary-based and sentiment analysis methods are used and the selected newspapers are examined with these principles. As a consequence of the quantitative analysis of these three English Taiwanese newspapers, it is shown that the media adopted a perspective similar to the views of the government and the DPP throughout the Tsai Ing-Wen period. Furthermore, sentiment analysis is done of the news from these newspapers relevant to PRC-Taiwan ties. It has been noted that the reporting regarding PRC-Taiwan ties in all three newspapers has a positive language.
Keywords: Taiwan, PRC, Text Analysis, Pro-Unification, Pro-Independence, KMT, DPP


RELATED LINK: http://daadtr.com/DAAD/ArchiveIssues/PDF/787eb20a-e1bb-ea11-810a-005056b0673e

2020 General Election in Taiwan

The results of the Taiwan general elections had been eagerly awaited for a long time. Especially the results of the local elections in 2018 made these general elections more remarkable. In addition, the protests in Hong Kong increased the importance of the Taiwan general elections.

In this article, I will first describe my observations in general elections. Then I will briefly explain the results of Twitter analysis about Taiwan general election, which I analyzed with the text analysis method.

OBSERVATION

In the morning, I started following the general elections in Taiwan. At the same time, I also tried to feel the political atmosphere inside country. Because of that reason, I talked with some of my Taiwanese friends. Around 7 PM, the election results began to be announced. Around 7.30pm, I first went to the KMT's headquarter in Kaohsiung. However, the KMT headquarter was quite empty. The most important reason for this was that DPP was ahead by far, according to the first results. Another important reason was that pan-blue party sympathizers did not trust their candidates.

Around 8 pm, the results began to be clear and I decided to go to the DPP's rally. The rally area was quite crowded. It was as crowded as the KMT's rally, which won last year's local elections. The excitement and joy of people in the rally area was quite interesting.

As a result of the local elections in 2018, the Green Party(DPP) lost the local elections. As a result of the defeat, the DPP leader and Taiwanese president Tsai Ing-Wen resigned from party leadership. But Tsai Ing-Wen, who withdrew her resignation, won this year's general elections.

With the results, the KMT candidate Han Kuo-yu made his speech first. About an hour later, Tsai Ing wen, the winner of the election, made her speech. However, the first thing that caught my attention during this speech was that Tsai Ing Wen spoke quite calmly and seriously. Unlike Ing Wen, DPP sympathizers were extremely happy.

In her speech, Ing wen praised Taiwan's democracy and stated that relations with China will continue in the same way.

TWITTER ANALYSIS

2000 English tweets about Taiwanese general elections between the January 1st and January 20th were examined with R computer program. First, the most commonly used words were found in these 2000 tweets. The words Economy, Growth, Hong Kong Protests, independence and victory are the most used words in 2000 tweets.

Most of the tweets are positive about Ing-Wen and DPP's victory.

Words like freedom, peaceful, democracy, growth and hope are the most commonly used positive words. Words such as infrared, protest, fight and Xi Jinping are the most commonly used negative words. 

Secondly, sentiment analysis of these 2000 tweets was analyzed. The highest sentiment in these tweets is trust. Then comes anticipation and fear.

Words like president, democracy, freedom, integrity, alliance and brilliant are the words with the highest trust sentiment. Words like young, vote, result and prevention are the words with the highest sense of anticipation. Words like Fight, Interfere, and Xi Jinping are the words with the highest sense of fear.

CONCLUSION

In conclusion, DPP sympathizers were very happy and hopeful at the DPP rally. Twitter analysis results gave similar results.

However, despite all this happiness and anticipation, it is a matter of curiosity how China-Taiwan relations and Ing-Wen's attitude towards China will change and shape.

Trend Topic Analysis with R

Twitter is one of the most popular social media applications in recent years. With the R program, it is possible to analyze many data from Twitter. Two of these analyzes are sentiment and polarity analysis.

These analyzes were applied to one of the trend topics on Twitter. For this purpose, the first trend topics in the world were found with "getTrend ()" function. As a results of this research, the most popular trend topic is #gretathunberg.

Afterwards, tweets were cleaned and sentiment and polarity analysis were performed.

First, polarity analysis was performed. As a results of this analysis, the majority of tweets related to #gretathumberg are positive.

Figure 1: polarity analysis

Secondly, sentiment analysis was performed. According to this analysis, anticipation and trust are the most dominant feelings among tweets.

Figure 2: Sentiment analysis (second column is anticipation.)

Finally, the most popular devices, which are used to post on twitter, were analyzed. Twitter web app and twitter android are the most used software.

Figure 3: Most used software and devices

Terrorism Dilemma

Terrorism has been one of the most remarkable issues in recent years. Many scholars argue about terrorism. However, the lack of a definition of terrorism complicates the issue.

The most acceptable definition of terror in the world is the CIA's definition of terror. This study uses the CIA's definition of terrorism. According to Central Intelligence Agency (2007), terrorism means premeditated, politically motivated violence perpetrated against noncombatant targets by subnational groups or clandestine agents, usually intended to influence an audience.

This definition is quite clear. Based on this definition, the YPG in Syria is a terrorist organization. However, many Western countries and the United States do not recognize YPG as terrorists. But this organization has caused the deaths of many innocent people in Turkey. Nevertheless, the media of many countries continue to have positive news about this organization. In addition, there are also positive news about the terrorist organization on Twitter.

For this purpose, sentiment and polarity analysis related with YPG in CNN, The New York Times and Twitter was performed.

                       

      

                                      Figure 1: Polarity result of CNN

Firstly, CNN's latest news about ypg is examined. The analysis revealed that the CNN's news was very positive about YPG. The most commonly used sentiment in the news about YPG is trust.

                               

                                          Figure 2: Sentiment result of CNN

            Secondly, the last news of the new york times about ypg was analyzed. Similar results were obtained with CNN. The new york times ypg news is also very positive. The most commonly used sentiment in the New York Times’ news about YPG is trust.

                                                Figure 3: Polarity result of New York Times

                                               Figure 4: Sentiment result of New York Times

Finally, the last 100 tweets about YPG on Twitter were analyzed. Twitter also has positive tweets about YPG, like CNN and New York Time. The most commonly used sentiment in twitter about YPG is fear. Tweets about ISIS raise the fear rate. That’s why fear was the most common sentiment in this anaysis.

                                      Figure 5: Polarity result of Tweets

                                      Figure 6: Sentiment result of Tweets

As seen above, there are positive views on YPG, which many countries recognize as terrorist organizations, in the media and social media.