A Framework of NLP Based Information Tracking and Related Knowledge Organizing with Topic Maps

Kawtrakul, Asanee; Yingsaeree, Chaiyakorn; Andrès, Frédéric

doi:10.1007/978-3-540-73351-5_24

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…In the information age, big data analytics play an important role in the interpretation of raw sensor data from the environment by harnessing intricate analytic techniques to discover hidden patterns, correlations, and other insights from a massive amount of data [53]. Several studies have explored ML, deep learning, text mining, statistical inference, and pattern recognition-driven big data analytics tools to reconstruct the state of the physical world from incoming sensor data [54], [55], [56]. Specifically, in the field of epidemiology, a good amount of effort has been contributed to developing big data analytics techniques for the identification and monitoring of communicable disease contacts using data collected from various sources (e.g., past disease records, self-reported symptom data, and wearable sensor data).…”

Section: Big Data Analyticsmentioning

confidence: 99%

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Rashid

Wang

2023

IEEE Trans. Comput. Soc. Syst.

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With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.

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Section: Big Data Analyticsmentioning

confidence: 99%

CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact Tracing

Rashid

Wang

2023

IEEE Trans. Comput. Soc. Syst.

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“…The growing demand for intelligent application domains like autonomous driving, robotics, computational medicine, computer vision, and natural language processing call for reliable AI-driven information distillation systems (Abiodun et al 2018). In the recent past, several studies have used AI for diagnosis, identification, and monitoring of infectious diseases using data collected from various sources (e.g., past disease records, social media posts, wearable sensors) (Barrat et al 2014;Kawtrakul et al 2007;Torres et al 2016). Babu et al applied Grey Wolf optimization and recurrent neural networks (RNN) on patient symptom data for early disease detection and response (Babu et al 2018).…”

Section: Ai-driven Disease Predictionmentioning

confidence: 99%

CovidSens: a vision on reliable social sensing for COVID-19

Rashid

Wang

2020

Artif Intell Rev

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With the spiraling pandemic of the Coronavirus Disease 2019 , it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic AI-driven sensing paradigm to extract real-time observations from online users. In this paper, we propose CovidSens, a vision of social sensing-based risk alert systems to spontaneously obtain and analyze social data to infer the state of the COVID-19 propagation. CovidSens can actively help to keep the general public informed about the COVID-19 spread and identify risk-prone areas by inferring future propagation patterns. The CovidSens concept is motivated by three observations: (1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, (2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media, and (3) online users are frequently equipped with substantially capable mobile devices that are able to perform non-trivial on-device computation for data processing and analytics. We envision an unprecedented opportunity to leverage the posts generated by the ordinary people to build a real-time sensing and analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions: How to distill reliable information about the COVID-19 with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively, and alert them to remain prepared? How to leverage the computational power on the edge devices (e.g., smartphones, IoT devices, UAVs) to construct fully integrated edge-based social sensing platforms for rapid detection of the COVID-19 spread? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in developing reliable social sensing-based risk alert systems. We envision that approaches originating from multiple disciplines (e.g., AI, estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.

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