Latent Twitter Image Information for Social Analytics

Razis, Gerasimos; Theofilou, Georgios; Anagnostopoulos, Ioannis

doi:10.3390/info12020049

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Social media data has been used in many humanitarian contexts ranging from general social analytics [32] and geospatial sentiment analysis [1] to incident detection [38] and rapid damage assessment [10], including multimodal approaches [25]. However, its use for landslide detection has not been explored extensively.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence

Ofli

Qazi

Imran

et al. 2022

Lecture Notes in Computer Science

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This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques. The designed system can (i) reduce the information overload by eliminating duplicate and irrelevant content, (ii) identify landslide images, (iii) infer geolocation of the images, and (iv) categorize the user type (organization or person) of the account sharing the information. The system was deployed in February 2020 online at https://landslide-aidr.qcri.org/ landslide_system.php to monitor live Twitter data stream and has been running continuously since then to provide time-critical information to partners such as British Geological Survey and European Mediterranean Seismological Centre. We trust this system can both contribute to harvesting of global landslide data for further research and support global landslide maps to facilitate emergency response and decision making.

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Section: Related Workmentioning

confidence: 99%

A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence

Ofli

Qazi

Imran

et al. 2022

Lecture Notes in Computer Science

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Landslide detection in real-time social media image streams

Ofli

Imran

Qazi

et al. 2023

Neural Comput & Applic

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Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response.

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Automating App Review Response Generation Based on Contextual Knowledge

Gao

Zhou

Xia

et al. 2021

ACM Trans. Softw. Eng. Methodol.

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User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of the method. In this article, we propose a novel neural network approach, named CoRe, with the contextual knowledge naturally incorporated and without involving external tools. Specifically, CoRe integrates two types of contextual knowledge in the training corpus, including official app descriptions from app store and responses of the retrieved semantically similar reviews, for enhancing the relevance and accuracy of the generated review responses. Experiments on practical review data show that CoRe can outperform the state-of-the-art method by 12.36% in terms of BLEU-4, an accuracy metric that is widely used to evaluate text generation systems.

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Latent Twitter Image Information for Social Analytics

Cited by 4 publications

References 23 publications

A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence

A Real-Time System for Detecting Landslide Reports on Social Media Using Artificial Intelligence

Landslide detection in real-time social media image streams

Automating App Review Response Generation Based on Contextual Knowledge

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