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With the growth of smartphone usage the number of social media posts has significantly increased and represents potentially valuable information for management, including of natural resources and the environment. Already, evidence of using 'human sensor' in crises management suggests that collective knowledge could be used to complement traditional monitoring. This research uses Twitter data posted from the Great Barrier Reef region, Australia, to assess whether the extent and type of data could be used to Great Barrier Reef organisations as part of their monitoring program. The analysis reveals that large amounts of tweets, covering the geographic area of interest, are available and that the pool of information providers is greatly enhanced by the large number of tourists to this region. A keyword and sentiment analysis demonstrates the usefulness of the Twitter data, but also highlights that the actual number of Reef-related tweets is comparatively small and lacks specificity. Suggestions for further steps towards the development of an integrative data platform that incorporates social media are provided., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Background: With ubiquitous social media platforms, people express their thoughts and emotions, making social media data valuable for studying and detecting depression symptoms. Objective: First, we detect depression by leveraging textual, visual, and auxiliary features from the Weibo social media platform. Second, we aim to comprehend the reasons behind the model's results, particularly in medicine, where trust is crucial. Methods: To address challenges such as varying text lengths and abundant social media data, we employ a text extension technique to standardize text length, enhancing model robustness and semantic feature learning accuracy. We utilize tree‐long short‐term memory and bidirectional gate recurrent unit models to capture long‐term and short‐term dependencies in text data, respectively. To extract emotional features from images, the integration of optical character recognition (OCR) technology with an emotion lexicon is employed, addressing the limitations of OCR technology in accuracy when dealing with complex or blurred text. In addition, auxiliary features based on social behaviour are introduced. These modalities' output features are fed into an attention fusion network for effective depression indicators. Results: Extensive experiments validate our methodology, showing a precision of 0.987 and recall rate of 0.97 in depression detection tasks. Conclusions: By leveraging text, images, and auxiliary features from Weibo, we develop text picture sentiment auxiliary (TPSA), a novel depression detection model. we ascertained that the emotional features extracted from images and text play a pivotal role in depression detection, providing valuable insights for the detection and assessment of the psychological disorder. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic triggered worldwide lockdowns and mobility restrictions, which continue to impact the global tourism industry. Early discussions on restarting the tourism industry occurred in high uncertainty constant change, and apprehension amongst political leaders and the public. Therefore, understanding public emotions and reactions toward border opening and international travel are essential. This research used the particular case of the 'travel bubble' between Australia and New Zealand to examine people's range of emotions as evident in social media. An automatic emotion analysis method was applied to identify eight emotions expressed through language. Furthermore, the content analysis was applied to detect key topics and understand potential triggers of the emotions. The theoretical background is appraisal theory which helps explain how a particular emotion was aroused. The results showed that people reacted positively to the travel bubble with anticipation, joy, and trust. Although fear peaked when the travel bubble temporarily paused, the confidence in the travel bubble was restored once a situation was resolved. By gauging people's emotions and concerns about the travel bubble, this research generates insights for tourism recovery and provides a method to gauge public emotions in future crises that affect international travel. [ABSTRACT FROM AUTHOR]

This research presents a new model based on Twitter posts and VADER algorithms to analyze social media discourse during and following a bushfire event. The case study is the Gold Coast community that experienced the first bushfire event of Australia's severe Black Summer in 2019/2020. This study aims to understand which communities and stakeholders generate and exchange information on disasters caused by natural hazards. In doing so, a new methodology to analyze social media in disaster management is presented. This model enables stakeholders to understand key message themes and community sentiment during and following the disaster, as well as the individuals and groups that shape the messaging. Three main findings emerged. Firstly, the results show that messaging volume is a proxy for the importance of the bushfires, with a clear increase during the bushfire event and a sharp decline after the event. Secondly, from a content perspective, there was a consistent negative message sentiment (even during recovery) and the need for better planning, while the links between bushfires and climate change were key message themes. Finally, it was found that politicians, broadcast media and public commentators were central influencers of social media messaging, rather than bushfire experts. This demonstrates the potential of social media to inform disaster response and recovery behavior related to natural hazards. [ABSTRACT FROM AUTHOR]

People provide information on their thoughts, perceptions, and activities through a wide range of channels, including social media. The wide acceptance of social media results in vast volume of valuable data, in variety of format as well as veracity. Analysis of such 'big data' allows organizations and analysts to make better and faster decisions. However, this data had to be quantified and information has to be extracted, which can be very challenging because of possible data ambiguity and complexity. To address information extraction, many analytic techniques, such as text mining, machine learning, predictive analytics, and diverse natural language processing, have been proposed in the literature. Recent advances in Natural Language Understanding-based techniques more specifically transformer-based architectures can solve sequence-to-sequence modeling tasks while handling long-range dependencies efficiently. In this work, we applied transformer-based sequence modeling on short texts' topic classification and sentiment analysis from user-posted tweets. Applicability of models is investigated on posts from the Great Barrier Reef tweet dataset and obtained findings are encouraging providing insight that can be valuable for researchers working on classification of large datasets as well as large number of target classes. [ABSTRACT FROM AUTHOR]

In the dynamic field of destination management, maintaining a destination's competitiveness requires understanding the evolving preferences of tourists. However, current research often adopts a static approach, failing to capture the dynamic nature of tourist needs and the evolving competitiveness of a destination. To address this, we introduce a novel approach using user-generated content from various social media platforms over a six-year period to assess key attributes that influence destination competitiveness. The results indicate that attributes in deciding destination competitiveness are not fixed, with some remaining stable factors in competitiveness, while others fluctuate over time. Attributes that even alter their competitive standing could significantly impact overall destination competitiveness. This research contributes a dynamic model that allows destination managers to adapt strategies in real time, aligning with current market conditions and enhancing competitiveness in the tourism industry. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for assessing visitor satisfaction at the destination level by identifying attributes of interest from social media text and indirectly measuring performance and importance values. A lexicon-based method for sentiment analysis is applied to determine performance value at the destination level, while importance is calculated using an adjusted association rule mining algorithm. The results are validated with earlier survey-based attributes relevant to visitors and Australia's case. The results demonstrate encouraging accuracy, suggesting that the proposed methodology offers opportunities to assess tourist satisfaction at destinations with larger sample sizes for a lower cost and greater data collection flexibility than traditional approaches. The methods proposed could be beneficial in a wide range of tourism contexts. • Destinations are recognized from tourists' travel itineraries. • Sentiment analysis is applied to identify performance at multiple destinations. • The importance of each attribute is calculated proportionally at each destination. • Validated results of social media data with survey data [ABSTRACT FROM AUTHOR]

Growing concern about the climate crisis comes with increased scrutiny of flying as an emission-intensive activity. This study conceptualises a modern communication system and draws on social media data to examine pre-COVID-19 (September to December 2019) public conversations about aviation and climate change. The analysis of >326,000 Twitter posts reveals that issue salience can be considerable at times, especially during key events such as United Nation climate summits and for users from countries such as Portugal, Puerto Rico, Qatar, France and Spain. Topic modelling identified five key frames: economy and industry, public opinion, fairness and conflict, consequences and responsibility. The fairness and conflict frame recorded the largest number of tweets, and was noteworthy for its negative sentiment. It also had the highest retweet rate and the densest network in terms of average users per community. The responsibility frame was interesting for its positive sentiment, and perhaps the opportunity for aviation stakeholders to contribute proactively to the conversations by sharing climate action success stories. Given the importance of public opinion (voters and consumers), and the link between media activity and policy decisions, the ongoing monitoring of issue salience, frames and peer groups related to flying and climate change seems beneficial. • Machine learning approach to monitoring social media discussions on aviation and climate change. •Aviation is a salient topic within climate change discussions on Twitter. •There are pronounced peaks in salience, often in association with key events. •Five frames have been identified from the aviation climate change social media activity. •The fairness and conflict frame is most prominent and negative and requires stakeholder attention. [ABSTRACT FROM AUTHOR]