| Full text | |
| Author(s): |
Giuntini, Felipe Taliar
[1, 2]
;
de Moraes, Kaue L.
[1]
;
Cazzolato, Mirela T.
[1]
;
Kirchner, Luziane de Fatima
[3]
;
Dos Reis, Maria de Jesus D.
[4]
;
Traina, Agma J. M.
[1]
;
Campbell, Andrew T.
[5]
;
Ueyama, Jo
[1]
Total Authors: 8
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos - Brazil
[2] Sidia Inst Sci & Technol, BR-13566590 Manaus, Amazonas - Brazil
[3] Univ Catolica Dom Bosco, Dept Psychol, BR-79117900 Campo Grande, MS - Brazil
[4] Univ Fed Sao Carlos, Dept Psychol, BR-13565905 Sao Carlos - Brazil
[5] Dartmouth Coll, Dept Comp Sci, Hanover, NH 03755 - USA
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | IEEE ACCESS; v. 9, p. 93182-93194, 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
The way users interact on social media can indicate their well-being. When depressed, people's feelings tend to be more evident, affecting how users interact and demonstrating their feelings on social media. This paper presents a new approach for the temporal assessment of emotional behavior and interaction among depressed users on social networks. We start by modeling user interactions using complex networks, grouping users through time using the Clauset-Newman-Moore greedy modularity maximization. We evaluate the built networks using metrics such as assortativity, density, clustering, diameter, and shortest path length, closeness, and coverage. Then, we propose EMUS, a method for establishing an emotional user score based on the extraction of emotional features in texts of posts and comments. To extract emotional features, we combine the use of the Empath framework and VADER lexicon. Finally, based on the standard deviation among users, we establish a metric for assessing mood levels. We evaluated users for 33 days, and the results show a sequence of mixed emotional behaviors with high correlations between the number of active users in the network communities, and the form and quality of interactions. The developed approach can be further applied to other database graphs, for different sequential pattern analysis and text-mining contexts. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 20/11258-2 - Interoperability and similarity queries on medical databases |
| Grantee: | Mirela Teixeira Cazzolato |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD |
| Grantee: | Mirela Teixeira Cazzolato |
| Support Opportunities: | Scholarships in Brazil - Technical Training Program - Technical Training |
| FAPESP's process: | 18/17335-9 - Exploring DLTs and Computational Intelligence in IoT |
| Grantee: | Jó Ueyama |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Regular Research Grants |