Abstract
Since the summer of 2015, the refugee crisis in Europe has grown to be one of the biggest challenges Europe has faced since WW2. The development of this humanitarian crisis are the topic of discussions throughout Europe and covered by media on a daily basis. Germany in particular has been the focus of migration. Over time, in Germany and the neighboring German speaking countries a shift could be observed, from the initial hospitable Willkommenskultur (welcome culture), to more reserved and skeptical points of view. These factors - Germany as the prime-destination for migrants, as well as a shift in public perception and media coverage - are the motivation for our analysis. The current article investigates the coverage of this crisis on traditional and social media, employing sentiment analysis to detect tendencies and relates these to real-world events. To this end, sentiment analysis was applied to textual documents of a data-set collected from relevant and highly circulated German, Austrian and Swiss traditional media sources and from social media in the course of six months from October 2015 to March of 2016.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Information about these events has been taken from http://zeitstrahl- flüchtlingskrise.org, providing excellent coverage and history of events concerning the refugee crisis, accessed on 2016/06/08.
- 2.
Pegida: Patriotische Europäer gegen Islamisierung des Abendlandes (Patriotic Europeans Against the Islamisation of the West), www.pegida.de.
- 3.
Germany: Passauer Neue Presse, Frankfurter Allgemeine, Focus, Welt, Spiegel; Austria: Der Standard, Kleine Zeitung, Salzburger Nachrichten, Die Presse, Wiener Zeitung; Switzerland: Neue Zürcher Zeitung, Aargauer Zeitung, Tagesanzeiger, Basler Zeitung, 20 Minuten.
References
Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the 1st ACM Conference on Online Social Networks (COSN 2013), Boston, USA, pp. 27–38. ACM (2013)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 79–86, Philadelphia, PA, USA (2002)
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)
Esuli, A., Sebastiani, F.: SENTIWORDNET: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 06), pp. 417–422 (2006)
Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle. In: ACL (System Demonstrations). pp. 115–120 (2012)
Gonçalves, P., Benevenuto, F., Cha, M.: PANAS-t: A Pychometric Scale for Measuring Sentiments on Twitter. CoRR abs/1308.1857 (2013)
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 25–54 (2010)
Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge, pp. 14–18 (2010)
Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11(4), 441–456 (2009)
Balahur, A., Steinberger, R., Kabadjov, M., Zavarella, V., van der Goot, E., Halkia, M., Pouliquen, B., Belyaeva, J.: Sentiment analysis in the news. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010), Valletta, Malta, ELRA (2010)
Coletto, M., Esuli, A., Lucchese, C., Muntean, C.I., Nardini, F.M., Perego, R., Renso, C.: Sentiment-enhanced multidimensional analysis of online social networks: perception of the mediterranean refugees crisis. In: Workshop on Social Network Analysis Surveillance Technologies (SNAST 16), San Francisco, USA (2016)
Remus, R., Quasthoff, U., Heyer, G.: SentiWS - a German-language resource for sentiment analysis. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC), Valletta, Malta, pp. 1168–1171 (2010)
Momtazi, S.: Fine-grained German sentiment analysis on social media. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012), Istanbul, Turkey, ELRA, pp. 1215–1220 (2012)
Shalunts, G., Backfried, G.: SentiSAIL: sentiment analysis in English, German and Russian. In: Perner, P. (ed.) MLDM 2015. LNCS (LNAI), vol. 9166, pp. 87–97. Springer, Heidelberg (2015). doi:10.1007/978-3-319-21024-7_6
Backfried, G., Göllner, J., Quirchmayr, G., Rainer, K., Kienast, G., Thallinger, G., Schmidt, C., Peer, A.: Integration of Media sources for situation analysis in the different phases of disaster management: the QuOIMA project. In: Proceedings of European Intelligence and Security Informatics Conference (EISIC 2013), Uppsala, Sweden, pp. 143–146 (2013)
Shalunts, G., Backfried, G., Prinz, K.: Sentiment analysis of German social media data for natural disasters. In: Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management (ISCRAM), University Park, Pennsylvania, USA, pp. 752–756 (2014)
Backfried, G., Schmidt, C., Pfeiffer, M., Quirchmayr, G., Glanzer, M., Rainer, K.: Open source intelligence in disaster management. In: Proceedings of the European Intelligence and Security Informatics Conference (EISIC), pp. 254–258, Odense, Denmark. IEEE (2012)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(8), 399–433 (2009)
Norman, G.J., Norris, C.J., Gollan, J., Ito, T.A., Hawkley, L.C., Larsen, J.T., Cacioppo, J.T., Berntson, G.G.: Current emotion research in psychophysiology: the neurobiology of evaluative bivalence. Emot. Rev. 3(3), 349–359 (2011)
Tan, C., Friggeri, A., Adamic, L.A.: Lost in propagation? Unfolding news cycles from the source. In: Proceedings of the 10th International AAAI Conference Web and Social Media (ICWSM 2016), Cologne, Germany (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Backfried, G., Shalunts, G. (2016). Sentiment Analysis of Media in German on the Refugee Crisis in Europe. In: DÃaz, P., Bellamine Ben Saoud, N., Dugdale, J., Hanachi, C. (eds) Information Systems for Crisis Response and Management in Mediterranean Countries. ISCRAM-med 2016. Lecture Notes in Business Information Processing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-47093-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-47093-1_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47092-4
Online ISBN: 978-3-319-47093-1
eBook Packages: Business and ManagementBusiness and Management (R0)