Digital Information Cascades and Sustainable Visitor Flow Management: Evidence from GPS Trajectories and Social Media During an Urban Festival

Urban festivals attract substantial numbers of tourists, which consequently imposes significant strain on host cities through spatial overcrowding, uneven pressure on infrastructure, and diminished quality of the visitor experience. Destination management organizations (DMOs) require effective tools to redistribute tourist flows; however, the influence of social media on tourists’ actual destination choices remains insufficiently understood. We ask whether social media discussion intensity (“buzz”) causally influences tourists’ destination choices and whether the effect grows stronger during festivals when information asymmetry is at its peak. Combining 95,692 taxi GPS trajectories with 5995 geotagged Twitter records from the 2019 Songkran Festival in Bangkok, we constructed an exponentially weighted moving average (EWMA) buzz variable with a temporal lag that establishes causal ordering. A conditional logit model shows that district-level buzz significantly raises destination choice probability and that the effect is amplified during the festival. Causal identification rests on a triangulated strategy that combines temporal lag, placebo permutation, and Bartik shift-share instrumental variables. The festival-period IV-corrected estimate ( β ^ IV = + 0.019 , p < 0.001 ) is 51% larger than the within-period OLS estimate ( β ^ OLS = + 0.012 , p < 0.001 ), a gap consistent with classical measurement-error attenuation in sparse social-media data, and a panel 2SLS analysis at the district–day level isolates a causal visitation channel confirming that cascades reinforce spatial concentration at the tourist-flow level. The aggregate Gini coefficient of spatial concentration declines over the study window in a statistically significant monotonic trend. The positive district-level correlation between buzz and congestion does not survive district and date fixed effects, which indicates that it reflects underlying differences in attractiveness across districts rather than a direct within-district channel. These findings provide an empirical foundation for information-based visitor flow management by identifying the underlying behavioral mechanism rather than evaluating a designed intervention.