{"language":[{"iso":"eng"}],"year":"2021","editor":[{"first_name":"Jonathan ","last_name":"Bright","full_name":"Bright, Jonathan "},{"first_name":"Anastasia ","last_name":"Giachanou","full_name":"Giachanou, Anastasia "},{"first_name":"Viktoria ","last_name":"Spaiser","full_name":"Spaiser, Viktoria "},{"full_name":"Spezzano, Francesca ","last_name":"Spezzano","first_name":"Francesca "},{"first_name":"Anna ","full_name":"George, Anna ","last_name":"George"},{"first_name":"Alexandra ","full_name":"Pavliuc, Alexandra ","last_name":"Pavliuc"}],"_id":"6951","conference":{"name":"Third Multidisciplinary International Symposium, MISDOOM 2021","location":"Virtual Event","start_date":"2021-09-21","end_date":"2021-09-22"},"status":"public","place":"Cham","author":[{"first_name":"Mattias","last_name":"Luber","full_name":"Luber, Mattias"},{"first_name":"Christoph","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-0616-1027/work/216468415","id":"264138","orcid":"0000-0003-0616-1027","full_name":"Weisser, Christoph","last_name":"Weisser"},{"last_name":"Säfken","full_name":"Säfken, Benjamin","first_name":"Benjamin"},{"last_name":"Silbersdorff","full_name":"Silbersdorff, Alexander","first_name":"Alexander"},{"full_name":"Kneib, Thomas","last_name":"Kneib","first_name":"Thomas"},{"first_name":"Krisztina","full_name":"Kis-Katos, Krisztina","last_name":"Kis-Katos"}],"citation":{"ieee":"M. Luber, C. Weisser, B. Säfken, A. Silbersdorff, T. Kneib, and K. Kis-Katos, “Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data,” in <i>Disinformation in Open Online Media. MISDOOM 2021</i>, Virtual Event, 2021, pp. 33–49.","alphadin":"<span style=\"font-variant:small-caps;\">Luber, Mattias</span> ; <span style=\"font-variant:small-caps;\">Weisser, Christoph</span> ; <span style=\"font-variant:small-caps;\">Säfken, Benjamin</span> ; <span style=\"font-variant:small-caps;\">Silbersdorff, Alexander</span> ; <span style=\"font-variant:small-caps;\">Kneib, Thomas</span> ; <span style=\"font-variant:small-caps;\">Kis-Katos, Krisztina</span>: Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data. In: <span style=\"font-variant:small-caps;\">Bright, J.</span> ; <span style=\"font-variant:small-caps;\">Giachanou, A.</span> ; <span style=\"font-variant:small-caps;\">Spaiser, V.</span> ; <span style=\"font-variant:small-caps;\">Spezzano, F.</span> ; <span style=\"font-variant:small-caps;\">George, A.</span> ; <span style=\"font-variant:small-caps;\">Pavliuc, A.</span> (Hrsg.): <i>Disinformation in Open Online Media. MISDOOM 2021</i>, <i>Lecture Notes in Computer Science, Vol. 12887</i>. Cham : Springer, 2021, S. 33–49","apa":"Luber, M., Weisser, C., Säfken, B., Silbersdorff, A., Kneib, T., &#38; Kis-Katos, K. (2021). Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data. In J. Bright, A. Giachanou, V. Spaiser, F. Spezzano, A. George, &#38; A. Pavliuc (Eds.), <i>Disinformation in Open Online Media. MISDOOM 2021</i> (pp. 33–49). Cham: Springer. <a href=\"https://doi.org/10.1007/978-3-030-87031-7_3\">https://doi.org/10.1007/978-3-030-87031-7_3</a>","bibtex":"@inproceedings{Luber_Weisser_Säfken_Silbersdorff_Kneib_Kis-Katos_2021, place={Cham}, series={Lecture Notes in Computer Science, Vol. 12887}, title={Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-87031-7_3\">10.1007/978-3-030-87031-7_3</a>}, booktitle={Disinformation in Open Online Media. MISDOOM 2021}, publisher={Springer}, author={Luber, Mattias and Weisser, Christoph and Säfken, Benjamin and Silbersdorff, Alexander and Kneib, Thomas and Kis-Katos, Krisztina}, editor={Bright, Jonathan  and Giachanou, Anastasia  and Spaiser, Viktoria  and Spezzano, Francesca  and George, Anna  and Pavliuc, Alexandra Editors}, year={2021}, pages={33–49}, collection={Lecture Notes in Computer Science, Vol. 12887} }","chicago":"Luber, Mattias, Christoph Weisser, Benjamin Säfken, Alexander Silbersdorff, Thomas Kneib, and Krisztina Kis-Katos. “Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data.” In <i>Disinformation in Open Online Media. MISDOOM 2021</i>, edited by Jonathan  Bright, Anastasia  Giachanou, Viktoria  Spaiser, Francesca  Spezzano, Anna  George, and Alexandra  Pavliuc, 33–49. Lecture Notes in Computer Science, Vol. 12887. Cham: Springer, 2021. <a href=\"https://doi.org/10.1007/978-3-030-87031-7_3\">https://doi.org/10.1007/978-3-030-87031-7_3</a>.","short":"M. Luber, C. Weisser, B. Säfken, A. Silbersdorff, T. Kneib, K. Kis-Katos, in: J. Bright, A. Giachanou, V. Spaiser, F. Spezzano, A. George, A. Pavliuc (Eds.), Disinformation in Open Online Media. MISDOOM 2021, Springer, Cham, 2021, pp. 33–49.","mla":"Luber, Mattias, et al. “Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data.” <i>Disinformation in Open Online Media. MISDOOM 2021</i>, edited by Jonathan  Bright et al., Springer, 2021, pp. 33–49, doi:<a href=\"https://doi.org/10.1007/978-3-030-87031-7_3\">10.1007/978-3-030-87031-7_3</a>.","ama":"Luber M, Weisser C, Säfken B, Silbersdorff A, Kneib T, Kis-Katos K. Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data. In: Bright J, Giachanou A, Spaiser V, Spezzano F, George A, Pavliuc A, eds. <i>Disinformation in Open Online Media. MISDOOM 2021</i>. Lecture Notes in Computer Science, Vol. 12887. Cham: Springer; 2021:33-49. doi:<a href=\"https://doi.org/10.1007/978-3-030-87031-7_3\">10.1007/978-3-030-87031-7_3</a>"},"page":"33-49","publisher":"Springer","date_updated":"2026-06-02T08:37:06Z","doi":"10.1007/978-3-030-87031-7_3","user_id":"220548","publication_identifier":{"isbn":["978-3-030-87030-0"],"eisbn":["978-3-030-87031-7"]},"type":"conference","publication":"Disinformation in Open Online Media. MISDOOM 2021","series_title":"Lecture Notes in Computer Science, Vol. 12887","publication_status":"published","quality_controlled":"1","title":"Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data","date_created":"2026-06-01T14:00:06Z"}