Mining crisis information: A strategic approach for detection of people at risk through social media analysis

Ragini, J. Rexiline; Anand, P. M. Rubesh; Bhaskar, Vidhyacharan

doi:10.1016/j.ijdrr.2017.12.002

Cited by 43 publications

(18 citation statements)

References 30 publications

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“…Social ties also affect family life, and such ties are weakened when people spend more time on social networking sites interacting with online friends. Furthermore, people who share their photos, videos, and other information on social networks may have a difficult time preventing others from reusing such content in an undesirable way [70]. Some scholars have described the structure that new media sites should follow, and they have discovered resemblances between social networks, individuals and community observations, and belief.…”

Section: The Negative Effects Of Social Mediamentioning

confidence: 99%

The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan

et al. 2019

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In today’s world, social media is playing an indispensable role on the learning behavior of university students to achieve sustainable education. The impact of social media on sustainable education is becoming an essential and impelling factor. The world has become a global village and technology use has made it a smaller world through social media and how it is changing instruction. This original study is amongst the few to perform a focalized investigation on revealing the relationship between positive and negative characteristics of social media and the learning attitude of university students for sustainable education. However, this study aims to examine the constructive and adverse factors that impact on students’ minds and how these helped students to share positive and negative aspects with others. It is increasingly noticeable that social networking sites and their applications present enormous benefits for as well as risks to university students and their implications on students’ psychological adjustment or learning behaviors are not well understood. This study adapted the cluster sampling method, and respondents participated from five selected regions. Researchers distributed 1013 questionnaires among the targeted sample of university students with an age range of 16 to 35 years, and they collected 831 complete/valid responses. This study applied the social gratification theory to examine students’ behavior practicing social media usage. This study specifically identified 18 adversarial and constructive factors of social media from the previous literature. The findings revealed that the usage of social media in Pakistan has a negative influence on a student’s behavior as compared to positive aspects. Results may not be generalized to the entire student community as findings are specific to the specific respondents only. This study presents a relationship between antithetical and creative characteristics of social media and exhibits avenues for future studies by facilitating a better understanding of web-based social network use.

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Section: The Negative Effects Of Social Mediamentioning

confidence: 99%

The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan

et al. 2019

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“…Word frequencies, bursty words [ 34 ], and NER-based rules [ 9 ] are commonly used in the literature for extracting high value information from social media information spaces. Fang et al [ 13 ] used word frequencies to observe the temporal evolution of disaster-related topics, assess disaster impact, and identify disaster hotspots.…”

Section: Related Workmentioning

confidence: 99%

“…Widely used IoT and SSNs technologies allow machines (e.g., sensors, vehicles, and industrial machines) and people to create digital information (which we refer to as IoT sensors and Social Media information spaces, respectively) from observations of the physical word and communicate that via the Internet. More specifically, IoT sensors are machines that automatically monitor the physical world, while human or social sensors are people that report their observations of situations and thoughts via postings on social media such as Twitter [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], Facebook [ 11 ], Reddit [ 12 ], Weibo [ 13 , 14 , 15 ], and TripAdvisor [ 16 ]. Today IoT and social media data represent a significant (if not the dominant) portion of the volume of Internet data traffic and offer a great opportunity to increase the scope and accuracy of situation awareness that is needed to understand and respond to virtually any situation.…”

Section: Introductionmentioning

confidence: 99%

Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces

Mirza

Georgakopoulos

Yavari

2022

Sensors

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Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.

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“…Much of the existing natural disaster research on using social media data have focused on various aspects, such as damage assessment [11,13,14,30] and event detection [31][32][33][34][35]. However, the application of social media data to improve the situation awareness of emergency responders is still in an early stage, and more efforts on this topic are needed [36]. De Albuquerque et al [15] introduced an approach that leverages authoritative data to identify disaster-related tweets (firsthand short text messages posted on Twitter).…”

Section: Social Media-based Approach For Disaster Situational Awarenessmentioning

confidence: 99%

Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data

Cheng

Zhang

et al. 2019

IJGI

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Complex natural disasters often cause people to suffer hardships, and they can cause a large number of casualties. A population that has been affected by a natural disaster is at high risk and desperately in need of help. Even with the timely assessment and knowledge of the degree that natural disasters affect populations, challenges arise during emergency response in the aftermath of a natural disaster. This paper proposes an approach to assessing the near-real-time intensity of the affected population using social media data. Because of its fatal impact on the Philippines, Typhoon Haiyan was selected as a case study. The results show that the normalized affected population index (NAPI) has a significant ability to indicate the affected population intensity. With the geographic information of disasters, more accurate and relevant disaster relief information can be extracted from social media data. The method proposed in this paper will benefit disaster relief operations and decision-making, which can be executed in a timely manner.

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Mining crisis information: A strategic approach for detection of people at risk through social media analysis

Cited by 43 publications

References 30 publications

The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan

The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan

Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces

Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data

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