Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data

conference paper Identifying Topical Shifts in Twitter Streams: An Integration of Non-Negative Matrix Factorisation, Sentiment Analysis, and Structural Break Models for Large-Scale Data published yes Mattias Luber author Christoph Weisser author 2641380000-0003-0616-1027 Benjamin Säfken author Alexander Silbersdorff author Thomas Kneib author Krisztina Kis-Katos author Jonathan Bright editor Anastasia Giachanou editor Viktoria Spaiser editor Francesca Spezzano editor Anna George editor Alexandra Pavliuc editor Third Multidisciplinary International Symposium, MISDOOM 2021 Springer2021Virtual Event eng Disinformation in Open Online Media. MISDOOM 2021 978-3-030-87030-010.1007/978-3-030-87031-7_3 33-49 Luber, Mattias ; Weisser, Christoph ; Säfken, Benjamin ; Silbersdorff, Alexander ; Kneib, Thomas ; Kis-Katos, Krisztina: 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. (Hrsg.): Disinformation in Open Online Media. MISDOOM 2021, Lecture Notes in Computer Science, Vol. 12887. Cham : Springer, 2021, S. 33–49 @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} } Luber, M., Weisser, C., Säfken, B., Silbersdorff, A., Kneib, T., & 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, & A. Pavliuc (Eds.), Disinformation in Open Online Media. MISDOOM 2021 (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> 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 Disinformation in Open Online Media. MISDOOM 2021, Virtual Event, 2021, pp. 33–49. 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.” Disinformation in Open Online Media. MISDOOM 2021, 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>. 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. Disinformation in Open Online Media. MISDOOM 2021. 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> 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 Disinformation in Open Online Media. MISDOOM 2021, 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>. 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. 69512026-06-01T14:00:06Z2026-06-02T08:37:06Z