AI-Driven real-time social media analytics for consumer trust enhancement.
| dc.contributor.author | Sharon | |
| dc.contributor.author | Gnanaroy, E. Rushit | |
| dc.contributor.author | Gupta, Anish | |
| dc.contributor.author | kizi, Sotivoldiyeva Sarvinoz Kahramon | |
| dc.contributor.author | Kannimuthu, S. | |
| dc.contributor.author | Ali, Guma | |
| dc.date.accessioned | 2026-05-07T09:56:10Z | |
| dc.date.available | 2026-05-07T09:56:10Z | |
| dc.date.issued | 2026-02-20 | |
| dc.description | This research contributes to Sustainable Development Goals particularly SDG 1 on poverty reduction, SDG 2 on zero hunger, and SDG 9 on innovation by improving agricultural knowledge access and extension services for rural farmers. It also supports National Planning Authority Uganda’s NDP IV agenda on agro-industrialization, rural livelihoods, knowledge integration, and inclusive agricultural modernization. | |
| dc.description.abstract | In a hyperconnected digital ecosystem, companies have never faced as much pressure to maintain consumer trust as they do at present, as content surrounding social media spreads at an alarming pace, along with opinions, feedback, and false information disseminating in real-time. Conventional monitoring approaches, based on batch processing and periodic review, are insufficient for monitoring the dynamism and rapid pace of online interactions. This factor often leads to sluggish reactions to reputational risks and consumer dissatisfaction. In response to this issue, we propose an AI-based, real-time social media analytics system that will continuously track multimodal data streams, including text, photos, and video, to detect signs of consumer mood and trust occurrences. The system combines contextual sentiment analysis using transformer-based natural language processing models, mapping social influence and information propagation through graph neural networks, and anomaly detection algorithms to detect sudden changes in perception or possible misinformation. Streaming pipelines based on distributed computing infrastructure can guarantee lowlatency processing, and real-time alerting and predictive modelling can ensure proactive engagement approaches to reduce risks as they occur. Throughout the simulations, the framework outperforms previous analytics systems, achieving accuracy, precision, recall, and F1-score scores in the 85-90% range across various social media platforms. The system enables organisations to react proactively to negative sentiment, avoid reputational loss, and strengthen consumer trust in real-time by delivering actionable insights and instant alerts. The findings affirm that the combination of developed AI and real-time data processing will provide a scalable technology-based methodology that can not only increase operational efficiency but also provide the company with a sound mechanism for maintaining consumer trust in the dynamic online environment. | |
| dc.identifier.citation | Gupta, A., Kannimuthu, S., Gnanaroy, E. R., & Ali, G. (2025, November). AI-Driven real-time social media analytics for consumer trust enhancement. In 2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS) (pp. 1142-1147). IEEE. | |
| dc.identifier.uri | https://dir.muni.ac.ug/handle/20.500.12260/971 | |
| dc.language.iso | en | |
| dc.publisher | IEEE | |
| dc.subject | Real-Time Analytics | |
| dc.subject | Social Media Monitoring | |
| dc.subject | Sentiment Analysis | |
| dc.subject | Transformer Models | |
| dc.subject | Graph Neural Networks | |
| dc.subject | Anomaly Detection | |
| dc.subject | Consumer Trust | |
| dc.title | AI-Driven real-time social media analytics for consumer trust enhancement. | |
| dc.type | Other |