Social Media based Analysis of Refugees in Turkey

Abstract

In this paper, we propose a method to nd out public social media accounts of refugees, and trace them back to infer temporal and spatial events.These data will have many future applications in planning and design of solutions for their problems and needs, starting from legislations and social ones, to architectural and housing needs.It will lead to better understanding of the obstacles they face which they fear to express in direct interviews and inquests. In this rst application, we present our method to retrieve information from social media, share the characteristics of the data gathered and perform initial analysis with a discussion of future opportunities and di culties.

Keywords

• information retrieval
• social media
• refugees

Introduction

After Arab spring and following wars, Turkey accommodates more than 3 millions of Syrian refugees [ 2 ]. Being also one of the accelerators of the Arab spring, social media usage have accompanied majority of the events [ 8 ]. Having a dataset of refugees in Turkey has the potential to facilitate a number of future studies.

To understand the obstacles and di culties the refugees face in their refuge countries, it is necessary to conduct questionnaires and interviews, which is di cult and even forbidden in some countries. Thus, an indicative database could be established through data retrieval from social networks, which can serve for better understanding of refugees needs, and the future planning to bridge the gap between what they have and need for better integration in their new societies.

This paper presents a tool and methodology to collect data from Twitter accounts, re ne them to de ne the Syrian refugees accounts, then trace their tweets back to collect a database to be analyzed by time and location across Turkey, de ning the trend topics occupied the refugees minds to react with, tweeting and expressing their opinions both on the issues of their home country and new refuge country. In results, those trends were shown in a graphical presentation which enables better understanding of their needs with better planning for future intervention to solve social, economical and maybe political refugee issues and aspects in Turkey the country hosting highest number of refugees in the world today[ 1 ].

As a result of the con ict occurred in Syria, many citizens have migrated abroad since 2010. As an example of refugees related work, the study in [ 5 ] explores the ideas of users towards refugees by means of collected tweets including #refugeesnotwelcome hashtag. The work in [ 7 ] also exploits tweets including same hashtag to understand the portrayal of male Syrian refugees on social media. To understand outcomes of an immigrant related event on society, the work in [ 6 ] studies the prediction of attitudes of US Twitter users towards Islam and Muslims subsequent to the tragic Paris terrorist attacks that occurred on November 13, 2015. As an interesting research, the study in [ 3 ] shows the propagation power of Twitter on users via "seminar users" who are social media users engaged in propaganda in support of a political entity. 2

Used Method

To retrieve information from refugee related social media accounts, we analyzed public Twitter activity using Twitter API1. The process is divided into four steps: Firstly, we tried to gure out a method to de ne the accounts of Syrian refugees in Turkey. Secondly, we traced back those chosen users' accounts. Thirdly, we analyzed the data and classi ed it into groups by location and year. Finally, a trend analysis was conducted to reveal the most important issues the refugees discussed for each year.

Syrians are the majority of refugees in Turkey, therefore, we checked the number of registered Syrian refugees in each city. Figure 1 shows the distribution of Syrian refugees in Turkey. Using a logarithmic color code, the image is generated according to the statistics in [ 4 ]. From Figure 1, it is clear that the majority of the refugees are hosted in speci c cities. The bold border shows the regions chosen in our study. The cities in these regions accommodates 90% of Syrian refugees while they only have 48% of the total Turkish population. Table 1 shows the number of refugees in chosen cities and the ratio of refugees to the city populations. Twitter API lets searching for recent (seven days) tweets according to keywords, location, and used language. To avoid a biased collection of data, we don't provide any keywords. We search for Arabic tweets in speci c locations wherein refugees are accommodated intensely.

For practical purposes we limited the search to 1000 tweets per region. Then, we extracted the individual user IDs posting these tweets. As one account can post multiple tweets, we have less amount of user accounts then the number of tweets. We performed this procedure twice with 4 days interval to increase the number of users. As a result we collected a total of 5707 twitter users who were active recently. Table 2 shows the number of discovered users in each region. Tracing back and ltering out irrelevant accounts. Twitter API lets accessing up to last 3200 of a user's tweets including retweets. Therefore, we gathered tweets of each user we extracted in the previous step until the limit is reached or there is no more tweet from that user. In one query, it is possible to access only 200 tweets, thus, we run multiple queries to collect the maximum possible number of tweets. Table 2 includes the number of tweets collected from each city.

Among these accounts, there are ones which do not belong to individual users but to press or companies for instance. These accounts mostly post with a very high frequency including a big ratio of retweets. This information is helpful to di erentiate the users we are interested in to analyze in this study. To analyze the development of refugee related issues in post-"Arab Spring" years, we focus more on the users for whom we have data that covers those years. Table 3 shows the number of accounts with the date of their oldest tweets collected. We have selected the users that were active in or before 2014. Analysis. Firstly we have excluded the tweets from 2018, only tweets up to the end of 2017 were analyzed, since in 2018, millions of tweets (3,678,739) were retrieved in less than 20 days which are unreliable, due to the fact that tweeting 3200 tweets in those days means more than 160 tweets per day, implying that these tweets are either not coming from a real user, or not expressing an individual user opinion. Moreover, we have excluded the Twitter accounts created or have all the activities after 2014 since these accounts are incapable to re ect the continuous change of refugees' conditions and needs.

With the aforementioned approach, out of 5707 accounts and 12 million tweets, we have re ned the accounts into 633 and the tweets to almost 800 thousands, and classi ed these tweets into: tweets (435,378) and RT( 336,753) using the tweets only we have created the word clouds seen in Figure 2. The analysis shown is an example which was conducted for the total tweets in Turkey. 3

Preliminary Results and Discussion

The word clouds shown in Figure 2 are created to show the trends and most frequent words in the collected data set. We used an Arabic light stemmer [ 9 ] to select the correct words and avoid pronouns, conjunction, su xes and non useful or meaningful words etc. A general view over the analyzed ve years shows that the main topic that occupied the refugees minds was the world's reaction to their tragedy, words like: world, hearts and friend express the disappointed hearts of Syrians towards the world which called himself Syrian peoples friend. Moreover, with all the su er of the refugees life during the war, peace was the third most repeated word in their tweets, which represents clearly their eagerness to end the war and establish permanent peace. While with a yearly based analysis, we noticed the change in topics across the years from deep involvement in Syrian news and issues in the rst four years to appear issues related with obstacles in Turkey such as work, study, dreaming of returning, beauty and achievements of Turkey and of course the coupe attempt and taking decision of immigrating to Europe or staying in Turkey....etc. Acts of same accounts such as using Turkish words in Arabic alphabet like Para which means (Money), then using Turkish 2012-2017 (Ar) 2012 (En) 2012 (Ar) 2015 (En) 2015 (Ar) 2013 (En) 2013 (Ar) 2016 (En) 2016 (Ar) 2014 (En) 2014 (Ar) 2017 (En) 2017 (Ar) Fig. 2: Generated wordclouds for years 2012 to 2017. Same wordclouds for each year presented: Original Arabic words (right), 20 most frequent words translated to English (left). language and alphabet in the tweets, re ects that those users have started to learn Turkish, as their integration process in the society has become deeper and stronger since their stay took longer than they expected at the beginning.In a location based analysis even clearer di erences in refugees situation and trending issues between Turkish cities could be concluded. Di erent elds and topics analysis can be applied to this data set in order to retrieve more information about speci c issues, such as services to refugees, legislative and logistics of their stay in the refuge countries, and reactions to politics change and treatment in hosting countries. The preliminary analysis shows accounts that tweets according to events, while other accounts are created to form the public opinions about speci c political topics. This provides wide potentials for deeper future analysis to better understanding the conditions of refugees, and to enhance the public plans for their integration. To facilitate further analysis, we share the dataset and our analysis online 2. To understand the cultural and social value of cultural heritage issues, future work will focus on analysis of cultural elements which the refugees were aware of and concerned about. To represent the collective identity of a community or nation, that has been forced to leave and refuge in other countries, by visual means is another future work direction. The interaction of locals with refugees' issues will also be studied and analyzed to compare the di erent treatment of the two communities in the hosting country.

References

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2 http://ybu.edu.tr/abulbul/custom page-283-refugees