An annotated dataset for identifying behaviour change based on five doors theory under coral bleaching phenomenon on Twitter

A tourism dataset from historical transaction for recommender systems

The tourism industry has currently grown in various aspects, including the types of attractions, their quantity, and the number of tourist visits in various regions, contributing positively to both regional and global economies. Historical transactions are essential for developing recommender systems, utilizing techniques such as Collaborative Filtering and Demographic Filtering. TripAdvisor is a reputable website providing a wide range of accessible tourism information, including attractions, user profiles, and ratings. However, this unstructured raw data requires processing to create an adequate dataset for recommender systems. This study conducted a series of data processing steps on the raw data, including data restructuring, validation, content addition, integration with Google Maps, normalization, and modeling. This study successfully produced an original dataset comprising User Transaction, Item or Attraction, Attraction Type, Continent, Region, Country, City, and Visiting Mode. It also includes an entity relational model for tourism in Indonesia, particularly in Bali, Malang, and Yogyakarta regions, based on various global user experiences. This dataset is adequate and essential for developing various models of tourism recommender systems such as using Collaborative Filtering.

A BERTweet-based design for monitoring behaviour change based on five doors theory on coral bleaching campaign

Coral reefs are very important ecosystem which are the foundation of all life on this earth, but now they are under threat. Coral bleaching are happening now at a serious rate and the ultimate goal of conservation effort toward this issue is behaviour change. One of the most important parts of conservation effort is monitoring. However, monitoring the success of the coral bleaching campaign on behaviour change requires extensive data collection so traditional methods are not effective because they require resources that may not be met. The goal of this study is to build fast and vast automation in analyzing the stage of behaviour change. Social media data has prospect to become good alternative to be used because social media usage is currently increasing every year, including Twitter. Therefore, an automatic classification model was designed which can identify the stages of behaviour change based on the Five Doors Theory on Twitter. Five Doors Theory define 5 stages of behavior change: Desirability, Enabling Context, Can Do, Buzz, and Invitation. The data was fetched through a trusted repository, Mendeley Data, with title "An Annotated Dataset for Identifying Behaviour Change Based on Five Doors Theory Under Coral Bleaching Phenomenon on Twitter". There are 1,222 tweets with keywords related to coral bleaching that have been annotated according to the behaviour change stages. There are two proposed designs: embedding extraction which utilizes the output of each encoder layer in BERTweet and stacking ensemble which uses several BERTweet models with different hyperparameters that are ensembled using a logistic regression model. The best accuracy of 0.7796 with an f1-score of 0.7945 was obtained in the stacking ensemble design scenario. The classification model created can identify each class at the stage of behaviour change well, even though the dataset is unbalanced in its distribution. The proposed design has a performance that exceeds all baseline models and the standalone BERTweet. In conclusion, the automatic classification model create the process of monitoring the stages of behavior change run effectively and efficiently so that the success of the coral bleaching campaign can be monitored and achieved.