Impact of social media-driven misinformation on project sustainability and public perception in mega projects
Bibliographic record
Abstract
The proliferation of social media has transformed the dissemination of information. Still, it has also facilitated the spread of misinformation, significantly influencing public perception and the sustainability of large-scale projects. This study examines the impact of social media-driven misinformation on project sustainability and public perception in Nigeria, with a focus on large-scale infrastructure projects. It explores how misinformation affects stakeholder trust, policy implementation, and project continuity. A qualitative research approach was adopted, focusing on the Abuja Municipal Area Council (AMAC) in the Federal Capital Territory (FCT), Nigeria. Purposive sampling was used to select 15 key stakeholders, including construction executives, government regulatory officials, and professional association members. Data were collected through semi-structured interviews and were analysed thematically. Findings indicate that misinformation weakens stakeholder confidence, disrupts project timelines, and complicates regulatory enforcement. Key institutions, including the National Orientation Agency (NOA), the Nigerian Communications Commission (NCC), and the Infrastructure Concession Regulatory Commission (ICRC), play crucial roles in addressing these challenges. The study highlights the need for stronger media literacy programmes, proactive public engagement strategies, and improved regulatory oversight. This research contributes to understanding the link between misinformation and governance in project management, reinforcing the importance of accurate information in ensuring the success and sustainability of mega projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".