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Simulating affective information diffusion using natural language in online social networks
Thesis (PhD)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867613751185768448 |
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| access_status_str | Open Access |
| author | Marais, Kurt |
| author2 | Venter, Lieschen |
| author_browse | Marais, Kurt Venter, Lieschen |
| author_facet | Venter, Lieschen Marais, Kurt |
| author_sort | Marais, Kurt |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/136259 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:41:06.301Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/136259 Simulating affective information diffusion using natural language in online social networks Marais, Kurt Venter, Lieschen Visagie, S. E. Merchant, S. N. Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Thesis (PhD)--Stellenbosch University, 2026. Marais, K. 2026. Simulating affective information diffusion using natural language in online social networks. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/0e580108-6db8-4b17-9619-e5e963df7be0 Social media platforms have fundamentally transformed how information spreads through populations, generating new challenges for understanding their impact on mental health. Anchored in the frameworks of emotional contagion and infodemiology, this dissertation investigates how affective information diffuses through online social networks and its implications for individuals with depression, proposing a novel expansion of infodemiology to encompass non-communicable mental health conditions. The methodology combines computational and qualitative approaches across two datasets from the Reddit and Twitter platforms. Reddit data from the r/depression subreddit were analysed to identify behavioural and linguistic markers associated with depression. This was achieved through content analysis, natural language processing techniques and thematic analysis, generating a body of domain-specific knowledge that extends beyond clinical diagnostic tools. Topic modelling techniques using latent Dirichlet allocation, non-negative matrix factorisation, Top2Vec and BERTopic revealed nuanced thematic patterns in discourse informed by lived depression experiences. A qualitative thematic analysis consolidated these findings into four major themes and 23 sub-themes, providing comprehensive domain knowledge of these lived experiences. From this, a depression-specific sentiment lexicon was constructed, demonstrating competitive performance against general-purpose lexica and even large language models when applied to depression-related and broader mental health data. Insights derived from Reddit informed the development of a novel discrete stochastic agentbased model of a Twitter network. In this simulation, sentiment-bearing interactions were modelled to evaluate the mechanisms by which affective information diffuses across social ties. This approach incorporates higher-order Markovian properties through historical agent states, as well as the influence from connections with other agents. The simulation revealed emotional reinforcement as the primary mechanism of affective transmission rather than simple contagion, with agents forming reinforcing groups that amplify existing emotional tendencies. Verification and validation procedures confirmed the robustness of the model, while scenario testing of network-level interventions, such as muting or blocking, yielded improvements in well-being but with heterogeneous effects across user groups. Individuals with depression demonstrated differential susceptibility, processing affective information differently from non-depressed users and showing particular vulnerability to reinforcement effects. The findings demonstrate that social media content carries meaning beyond its informational value, exerting a direct impact on mental health outcomes. This research expands the scope of infodemiology beyond traditional disease surveillance to encompass the direct health effects of information exposure. By extending infodemiology to non-communicable conditions, this study makes three primary contributions through providing a methodological framework for modelling affective information diffusion, the construction of a depression-specific sentiment lexicon and a novel application of agent-based simulation to affective information diffusion as it relates to depression. The implications extend to multiple stakeholders: researchers gain computational tools for studying mental health in online contexts, practitioners obtain insights into contemporary depression experiences, platform developers receive evidence-based recommendations for promoting user well-being and social media users benefit from understanding their agency within algorithmic environments. As social media continues to evolve and data access becomes increasingly restricted, this research provides timely evidence for the urgent need to prioritise mental health considerations in digital platform design and public health discourse. Doctoral 2026-04-29T14:20:32Z 2026-04-29T14:20:32Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/136259 en Stellenbosch University 312 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Marais, Kurt Simulating affective information diffusion using natural language in online social networks |
| title | Simulating affective information diffusion using natural language in online social networks |
| title_full | Simulating affective information diffusion using natural language in online social networks |
| title_fullStr | Simulating affective information diffusion using natural language in online social networks |
| title_full_unstemmed | Simulating affective information diffusion using natural language in online social networks |
| title_short | Simulating affective information diffusion using natural language in online social networks |
| title_sort | simulating affective information diffusion using natural language in online social networks |
| url | https://scholar.sun.ac.za/handle/10019.1/136259 |
| work_keys_str_mv | AT maraiskurt simulatingaffectiveinformationdiffusionusingnaturallanguageinonlinesocialnetworks |