An Eye on Riyadh Tourist Season: Using Geo-tagged Snapchat Posts to Analyse Tourists Impression

Alghamdi, Najwa; Alageeli, Nourah; Sharkh, Doaa Abu; Alqahtani, Maram; Al-Razgan, Muna

doi:10.1109/iccis49240.2020.9257676

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…Sentiment distribution. Consistent with prior work on Snap Our Story data (Alghamdi et al 2020), the sentiment distribution is imbalanced, with the majority of Snaps being positive (52% positive, 33% neutral, and 15% negative). We found the same imbalance in the Twitter/Instagram data (64% positive, 29% neutral, and 7% negative).…”

Section: Appendixsupporting

confidence: 78%

Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media?

Jiang

Murrugara-Llerena

Bos

et al. 2022

ICWSM

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The environment we are in can affect our mood and behavior. One environmental factor is weather, which is linked to sentiment as expressed on social media. However, less is known about how integrating changes in weather, along with time and location contextual cues, can improve sentiment detection and understanding. In this paper, we explore the effects of three contextual features--weather, location, and time--on expressed sentiment in social media. Leveraging a large Snapchat dataset, we provide extensive experimental evidence that including contextual features in addition to textual features significantly improves textual sentiment detection performance by 3% over transformer-based language models. Our results also generalize cross-domain to Twitter. Ablation studies indicate the relative importance of weather compared to location and time. We also conduct correlation analyses on 8 million Snapchat posts to highlight the link between past weather and current sentiment, showing that weather has a lasting impact on mood. Users generally exhibit more positive sentiment in better weather conditions as well as in improved weather conditions. Additionally, we show that temperature's link with mood holds after controlling for time or population density, but there exist geographical differences in how temperature affects mood. Our work demonstrates the effectiveness of including external contexts in linguistic tasks and carries design implications for researchers and designers of social media.

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Section: Appendixsupporting

confidence: 78%

Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media?

Jiang

Murrugara-Llerena

Bos

et al. 2022

ICWSM

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“…Hence, for enhancing tourism, this article proposes an MQ-LSTM-based tourism model for Riyadh, the capital city of Saudi Arabia. Since the events have a strong as well as wide cultural effect on the local society, the cultural events of Riyadh are considered in this research [15], which could attract tourists.…”

Section: Introductionmentioning

confidence: 99%

Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security

Jeribi,

Ahamed,

Perumal

et al. 2023

Sustainability

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Cultural tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for peak visitor time in Riyadh, a city in Saudi Arabia. The main objective of the framework is to improve the cultural tourism of Riyadh by considering various factors to help in improving CT based on recommendation system (RS). Primarily, the map data and cultural event dataset were processed for location, such as grouping with Kriging interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes were processed with word embedding. Meanwhile, the social network sites (SNS) data like reviews and news were extracted with an external application programming interface (API). The review data were processed with keyword extraction and word embedding, whereas the news data were processed with score value estimation. Lastly, the data were fused, corresponding to a historical site, and given to the Multi-Quadratic-Long Short-Term Memory (MQ-LSTM) recommendation system (RS); also, the recommended result with the map was stored in a database. Lastly, the database security was maintained with locality sensitive hashing (LSH). From the experimental evaluation with multiple databases including the Riyadh Restaurants 20K dataset, the proposed recommendation model achieved a recommendation rate (RR) of 97.22%, precision of 97.7%, recall of 98.27%, and mean absolute error (MAE) of 0.0521. This result states that the proposed RS provides higher RR and reduced error compared to existing related RSs. Thus, by attaining higher performance values, the proposed model is experimentally verified.

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Recent trends in crowd management using deep learning techniques: a systematic literature review

Alasmari,

Farooqi,

Alotaibi

2024

J. Umm Al-Qura Univ. Eng.Archit.

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Crowd management has become an integral part of urban planning in abnormality in the crowd and predict its future issues. Big data in social media is a rich source for researchers in crowd data analysis. In this systematic literature review (SLR), modern societies. It can organize the flow of the crowd, perform counting, recognize the related works are analyzed, which includes crowd management from both global and local sides (Hajj events—Saudi Arabia) based on deep learning (DL) methods. This survey concerns crowd management research published from 2010 to 2023. It has specified 45 primary studies that accomplish the objectives of the research questions (RQs), namely, investigation of the taxonomies, approaches, and comprehensive studies of crowd management both globally and locally and focusing on the most commonly used techniques of DL. We found both supervised and unsupervised DL techniques have achieved high accuracy, with different strengths and weaknesses for each approach. A lot of these studies discuss aspects of scene analysis of crowds, that are captured by installed cameras in the place. However, there is a dilemma regarding exploiting data provided on social media to use in the crowd analysis domain. Which we believe that the analysis of big data may raise crowd management to the upper level of enhancement. To this end, motivated by the findings of this SLR. The primary purpose of this review is strived to illustrate obstacles and dilemmas in crowd analysis fields to provide a road map for future researchers. Furthermore, it aims to find research gaps existing to focus on it in the future studies. The results indicate that the lack of Hajj research, especially in sentiment analysis and the study of the pilgrims' behavior.

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An Eye on Riyadh Tourist Season: Using Geo-tagged Snapchat Posts to Analyse Tourists Impression

Cited by 8 publications

References 7 publications

Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media?

Sunshine with a Chance of Smiles: How Does Weather Impact Sentiment on Social Media?

Developing an MQ-LSTM-Based Cultural Tourism Accelerator with Database Security

Recent trends in crowd management using deep learning techniques: a systematic literature review

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