Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | CSCW 2021 |
| Untertitel | Conference Companion Publication of the 2021 Computer Supported Cooperative Work and Social Computing |
| Herausgeber (Verlag) | Association for Computing Machinery (ACM) |
| Seiten | 95-98 |
| Seitenumfang | 4 |
| ISBN (elektronisch) | 9781450384797 |
| Publikationsstatus | Veröffentlicht - 23 Okt. 2021 |
| Veranstaltung | 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2021 - Virtual, Online, USA / Vereinigte Staaten Dauer: 23 Okt. 2021 → 27 Okt. 2021 |
Publikationsreihe
| Name | Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW |
|---|
Abstract
Prior studies, mostly from the social science domain, have observed that mental stress and struggles are high for refugees. Information science researchers have found that social media data can be insightful for probing psychological stress. However, none of the previous studies, to the best of our knowledge, investigated social media data to identify the voices of migrants and refugees and analyzed their concerns. We have collected 0.15 million tweets, but only 2% of these tweets are the voices of migrants and refugees. In addition to non-refugee and non-migrant voices, we have classified their voices into three themes as follows: their generic views, initial struggles, and subsequent settlement in the host country. We have employed deep learning and transformer-based models for identifying these themes. Our best-performing transformer-based model has reported an accuracy of 75.89%. We have also identified some exciting avenues for future research.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Mensch-Maschine-Interaktion
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
CSCW 2021: Conference Companion Publication of the 2021 Computer Supported Cooperative Work and Social Computing. Association for Computing Machinery (ACM), 2021. S. 95-98 (Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Struggle to Settle down! Examining the Voices of Migrants and Refugees on Twitter Platform
AU - Khatua, Aparup
AU - Nejdl, Wolfgang
N1 - Funding Information: Funding for this paper was, in part, provided by the European Union’s Horizon 2020 research and innovation program under Grant Agreement No: 832921.
PY - 2021/10/23
Y1 - 2021/10/23
N2 - Prior studies, mostly from the social science domain, have observed that mental stress and struggles are high for refugees. Information science researchers have found that social media data can be insightful for probing psychological stress. However, none of the previous studies, to the best of our knowledge, investigated social media data to identify the voices of migrants and refugees and analyzed their concerns. We have collected 0.15 million tweets, but only 2% of these tweets are the voices of migrants and refugees. In addition to non-refugee and non-migrant voices, we have classified their voices into three themes as follows: their generic views, initial struggles, and subsequent settlement in the host country. We have employed deep learning and transformer-based models for identifying these themes. Our best-performing transformer-based model has reported an accuracy of 75.89%. We have also identified some exciting avenues for future research.
AB - Prior studies, mostly from the social science domain, have observed that mental stress and struggles are high for refugees. Information science researchers have found that social media data can be insightful for probing psychological stress. However, none of the previous studies, to the best of our knowledge, investigated social media data to identify the voices of migrants and refugees and analyzed their concerns. We have collected 0.15 million tweets, but only 2% of these tweets are the voices of migrants and refugees. In addition to non-refugee and non-migrant voices, we have classified their voices into three themes as follows: their generic views, initial struggles, and subsequent settlement in the host country. We have employed deep learning and transformer-based models for identifying these themes. Our best-performing transformer-based model has reported an accuracy of 75.89%. We have also identified some exciting avenues for future research.
UR - http://www.scopus.com/inward/record.url?scp=85118530743&partnerID=8YFLogxK
U2 - 10.1145/3462204.3481773
DO - 10.1145/3462204.3481773
M3 - Conference contribution
AN - SCOPUS:85118530743
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 95
EP - 98
BT - CSCW 2021
PB - Association for Computing Machinery (ACM)
T2 - 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2021
Y2 - 23 October 2021 through 27 October 2021
ER -