Migrants vs. stayers in the pandemic - A sentiment analysis of Twitter content

被引:3
作者
Czeranowska, Olga [1 ]
Chlasta, Karol [2 ]
Milkowski, Piotr [3 ]
Grabowska, Izabela [2 ]
Kocon, Jan [3 ]
Hwaszcz, Krzysztof [4 ]
Wieczorek, Jan [3 ]
Jastrzebowska, Agata [2 ]
机构
[1] SWPS Univ Social Sci & Humanities, Chodakowska 19-31, PL-03815 Warsaw, Poland
[2] Kozminski Univ, Warsaw, Poland
[3] Wroclaw Univ Sci & Technol, Wroclaw, Poland
[4] Univ Wroclaw, Wroclaw, Poland
来源
TELEMATICS AND INFORMATICS REPORTS | 2023年 / 10卷
关键词
Sentiment analysis; Text mining; Text analytics; Social media; Twitter; Migrants; SOCIAL MEDIA; COVID-19; HEALTH; GEOLOCATION; COMMUNITY; NETWORKS;
D O I
10.1016/j.teler.2023.100059
中图分类号
G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];
学科分类号
1205 ; 120501 ;
摘要
In this paper, we propose a sentiment analysis of Twitter data focused on the attitudes and sentiments of Polish migrants and stayers during the pandemic. We collected 9 million tweets and retweets between January and August 2021, and analysed them using MultiEmo, the multilingual, multilevel, multi-domain sentiment analysis corpus. We discovered that the sentiment of tweets differs between migrants and stayers over time, and it relates to the country of migration. The general sentiment is similar for migrants and stayers, but a more detailed analysis reveals that hashtags related to staying safe and staying at home, as well as vaccinations are more polarised for migrants than for stayers, and they reflect the general development trend of the pandemic in Europe. In addition to comparing migrants with stayers, we also compared migrants staying in different countries. amongst the countries of migration, for which we collected at least 3000 tweets, the most positive sentiment of Polish migrants' tweets was observed in Belgium, with the most negative sentiment coming from Estonia. We also observed that the sentiment of tweets written in Polish by stayers in Poland is less negative when compared to Polish migrants in most of the countries with the highest number of tweets.
引用
收藏
页数:13
相关论文
共 108 条
[1]   Twitter Sentiment Analysis Approaches: A Survey [J].
Adwan, Omar Y. ;
Al-Tawil, Marwan ;
Huneiti, Ammar M. ;
Shahin, Rawan A. ;
Abu Zayed, Abeer A. ;
Al-Dibsi, Razan H. .
INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING, 2020, 15 (15) :79-93
[2]   COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data [J].
Ahmed, Wasim ;
Vidal-Alaball, Josep ;
Downing, Joseph ;
Lopez Segui, Francesc .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2020, 22 (05)
[3]   Refugees and COVID-19: achieving a comprehensive public health response [J].
Alemi, Qais ;
Stempel, Carl ;
Siddiq, Hafifa ;
Kim, Eunice .
BULLETIN OF THE WORLD HEALTH ORGANIZATION, 2020, 98 (08) :510-510
[4]  
[Anonymous], 2020, DTM-Covid19 Travel Restrictions Output -19 March 2020
[5]  
[Anonymous], 2018, FREQUENTLY REQUESTED
[6]   Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic [J].
Arpaci, Ibrahim ;
Alshehabi, Shadi ;
Al-Emran, Mostafa ;
Khasawneh, Mahmoud ;
Mahariq, Ibrahim ;
Abdeljawad, Thabet ;
Hassanien, Aboul Ella .
CMC-COMPUTERS MATERIALS & CONTINUA, 2020, 65 (01) :193-203
[7]   Twitter chirps for Syrian people: Sentiment analysis of tweets related to Syria Chemical Attack [J].
Bashir, Saimah ;
Bano, Shohar ;
Shueb, Sheikh ;
Gul, Sumeer ;
Mir, Aasif Ahmad ;
Ashraf, Romisa ;
Shakeela ;
Noor, Neelofar .
INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 2021, 62
[8]   COVID-19: challenges faced by Nepalese migrants living in Japan [J].
Bhandari, Divya ;
Kotera, Yasuhiro ;
Ozaki, Akihiko ;
Abeysinghe, Sudeepa ;
Kosaka, Makoto ;
Tanimoto, Tetsuya .
BMC PUBLIC HEALTH, 2021, 21 (01)
[9]   Using social media influencers to increase knowledge and positive attitudes toward the flu vaccine [J].
Bonnevie, Erika ;
Rosenberg, Sarah D. ;
Kummeth, Caitlin ;
Goldbarg, Jaclyn ;
Wartella, Ellen ;
Smyser, Joe .
PLOS ONE, 2020, 15 (10)
[10]  
Borkert M., 2009, The State of the Art of Research in the EU On the Uptake and Use of ICT By Immigrants and Ethnic Minorities (IEM)