- Title
- ANSWER : generating information dissemination network on campus
- Creator
- Qing, Qing; Guo, Teng; Zhang, Dongyu; Xia, Feng
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176134
- Identifier
- vital:15079
- Identifier
-
https://doi.org/10.1007/978-3-030-69377-0_7
- Identifier
- ISBN:0302-9743 (ISSN); 9783030693763 (ISBN)
- Abstract
- Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 32nd Australasian Database Conference, ADC 2021 Vol. 12610 LNCS, p. 74-86
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ Springer Nature Switzerland AG 2021
- Subject
- Attribute network; Information dissemination; Network generation
- Full Text
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