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

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:10.1007/978-3-030-87031-7_3

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. https://doi.org/10.1007/978-3-030-87031-7_3

@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={10.1007/978-3-030-87031-7_3}, 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, 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. https://doi.org/10.1007/978-3-030-87031-7_3.

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:10.1007/978-3-030-87031-7_3.