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Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

This study aims to explore the impact of architecture on digital communication mediums, focusing on how social media shapes the public perception and discussion of architectural spaces. It specifically examines the case of the Basilica Cistern/Istanbul, analysing social media interactions post-restoration.

Using newspaper archive scanning and survey technique, this study observed public content on Instagram focusing on the post-restoration period of the Basilica Cistern. 406 (283 valid) people who visited the Cistern and shared their experiences on Instagram between August 2022 and January 2023 participated in a survey. The analysis utilized Python for advanced correlation studies, enabling an in-depth exploration of the interplay between architectural features and social media sharing behaviours.

The analysis revealed that historical significance, lighting elements, role as a photographic backdrop significantly influenced sharing behaviours. Correlations were found between specific spatial features of the cistern and various sharing motivations, such as communication with people, personal gain, and popularity. The study highlights a diverse spectrum of motivations among users, emphasizing the relationship between these motivations and spatial features.

This study underscores the necessity for further inquiry into the intricate dynamics among digital communication, architectural spaces, and user motivations. Limitations include potential challenges in gathering data from social media due to concerns of cyber fraud and the misuse of hashtags.

This research offers novel insights into the interplay between digital communication and architecture. It underscores the potential of digital platforms as valuable data sources for architectural theorizing and practice, particularly in understanding how restorations and architectural changes are perceived and discussed in the digital space.

As a distinctive form of communist heritage tourism, the ideology and government-led form of red tourism warrants an in-depth examination of how tourists consume and perceive it. This study aims to reveal tourists’ perception of red tourism through the lens of destination image.

This study collected 9,819 user-generated photographs within four types of red tourism destinations (RTDs) and used a computer visual and semiotic analysis approach to conduct photograph-based cognitive and affective attributes extraction. Network analysis further visualized the co-relations between cognitive images and affective images. ANOVA analysis compared the differences of the four types of destination images.

Ten dimensions of cognitive image and eight categories of affective image of red tourism were identified. It found that monuments, statues, memorial symbols were the distinctive cognitive features, and admiration was the most dominant emotion. Heterogeneity of destination images was identified among the four types of RTDs.

To the best of the authors’ knowledge, the study is one of the first to explore tourists’ consumption of red tourism through the lens of destination image, which reveals the inconsistencies between the officially projected images and tourists’ perceived images of red tourism. Using Plutchik’s model, it validates a series of positive and negative emotions contributing to the affective images of red tourism, which expands the findings of emotions within the extant red tourism research. Through combined applications of computer visual and semiotic analysis, ANOVA, network analysis and model visualization, the study provides an important methodological triangulation for photograph-based destination image studies.

红色旅游作为共产主义旅游的独特形式, 游客如何感知这种国家意识形态植入与政府主导型旅游值得深入研究。本研究旨在从目的地意象视角揭示游客红色旅游感知。

本研究收集四种类型的红色旅游地9819张用户生成照片, 利用计算机视觉-情感析法对照片进行认知和情感元素提取。复杂网络分析揭示了认知意象与情感意象之间的关联。方差分析比较了四种红色旅游地意象的差异。

本研究确定了红色旅游认知意象的十个维度和情感意象的八个类别。研究发现, 纪念碑、雕像、纪念符号是其独特的认知意象元素, 钦佩是其最主要的情感，四种类型红色旅游地意象存在差异性。

本文是同类研究中首次从目的地意象视角探索游客对红色旅游地感知, 揭示了红色旅游官方投射意象与游客感知意象之间的差异。利用Plutchik情感之轮模型, 验证了一系列积极和消极情绪构成红色旅游地情感意象, 拓展了红色旅游的情感发现。综合运用计算机视觉-情感分析、方差分析、网络分析和模型可视化等方法, 为基于照片的旅游目的地意象研究提供了一个重要方法。

Como forma distintiva del turismo del patrimonio comunista, la ideología y la forma gubernamental del turismo rojo justifican un examen en profundidad de cómo lo consumen y perciben los turistas. Este estudio pretende revelar la percepción que tienen los turistas del turismo rojo desde la perspectiva de la imagen del destino.

Este estudio recopiló 9.819 fotografías generadas por los usuarios dentro de cuatro tipos de destinos de turismo rojo, y utilizó un enfoque de análisis visual y semiótico por ordenador para llevar a cabo la extracción de atributos cognitivos y afectivos basados en fotografías. El análisis de redes visualizó además las correlaciones entre las imágenes cognitivas y las imágenes afectivas. El análisis ANOVA comparó las diferencias de los cuatro tipos de imágenes de destino.

Se identificaron diez dimensiones de imagen cognitiva y ocho categorías de imagen afectiva del turismo rojo. Se descubrió que los monumentos, las estatuas y los símbolos conmemorativos eran los rasgos cognitivos distintivos, y la admiración la emoción más dominante. Se identificó una heterogeneidad de imágenes de destino entre los cuatro tipos de destinos de turismo rojo.

El estudio es uno de los primeros en explorar el consumo de turismo rojo por parte de los turistas a través de la lente de la imagen del destino, lo que revela las incoherencias entre las imágenes proyectadas oficialmente y las imágenes percibidas por los turistas del turismo rojo. Utilizando el modelo de Plutchik, valida una serie de emociones positivas y negativas que contribuyen a las imágenes afectivas del turismo rojo, lo que amplía los hallazgos sobre las emociones dentro de la investigación existente sobre el turismo rojo. Mediante aplicaciones combinadas de análisis visual y semiótico por ordenador, ANOVA, análisis de redes y visualización de modelos, el estudio proporciona una importante triangulación metodológica para los estudios de la imagen del destino basados en fotografías.