Szczegóły publikacji
Opis bibliograficzny
Community aware models of meme spreading in micro-blog social networks / Mikołaj KROMKA, Wojciech CZECH, Witold DZWINEL // W: Computational Science - ICCS 2020 : 20th International Conference : Amsterdam, The Netherlands, June 3–5, 2020 : proceedings, Pt. 1 / eds. Valeria V. Krzhizhanovskaya, [et al.]. — Cham : Springer Nature Switzerland, cop. 2020. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12137. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-50370-3; e-ISBN: 978-3-030-50371-0. — S. 623–637. — Bibliogr. s. 636–637, Abstr. — Publikacja dostępna online od: 2020-06-15
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129173 |
|---|---|
| Data dodania do BaDAP | 2020-06-25 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-50371-0_46 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 20th International Conference on Computational Science |
| Czasopisma/serie | Theoretical Computer Science and General Issues, Lecture Notes in Computer Science |
Abstract
We propose the new models of meme spreading over social network constructed from Twitter mention relations. Our models combine two groups of diffusion factors relevant for complex contagions: network structure and social constraints. In particular, we study the effect of perceptive limitations caused by information overexposure. This effect was not yet measured in the classical models of community-aware meme spreading. Limiting our study to hashtags acting as specific, concise memes, we propose different ways of reflecting information overexposure: by limited hashtag usage or global/local increase of hashtag generation probability. Based on simulations of meme spreading, we provide quantitative comparison of our models with three other models known from literature, and additionally, with the ground truth, constructed from hashtag popularity data retrieved from Twitter. The dynamics of hashtag propagation is analyzed using frequency charts of adoption dominance and usage dominance measures. We conclude that our models are closer to real-world dynamics of hashtags for a hashtag occurrence range up to 104 .