Identification of Landscape Preferences by Using Social Media Analysis

Rai, Ankit; Minsker, Barbara; Diesner, Jana; Karahalios, Karrie; Sun, Yicheng

doi:10.1109/socialsens.2018.00021

Cited by 6 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, training algorithms still require a sufficiently large training data set, which can be quite laborious to generate. Rai et al () showed how distributed intelligence (Level 2 of Figure ), recruited using Amazon Mechanical Turk, can be used for generating a large training data set for identifying green storm water infrastructure in Flickr and Instagram images. More widespread use of such tools will be needed to enable rapid processing of large crowdsourced image and video data sets.…”

Section: Review Of Issues Associated With Crowdsourcing Applicationsmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

Self Cite

113

View full text Add to dashboard Cite

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

show abstract

Section: Review Of Issues Associated With Crowdsourcing Applicationsmentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

Self Cite

113

View full text Add to dashboard Cite

show abstract

“…Another application is environmental monitoring, either by mapping animal and plant species [99] or by assessing landscape preferences [100] and aesthetics [81], [101]. Urban areas can especially benefit from this data by collecting citizen sentiments and opinions from social media image content and textual descriptions [102].…”

Section: B Methodsmentioning

confidence: 99%

Geoinformation Harvesting From Social Media Data: A community remote sensing approach

Zhu

Wang

Kochupillai

et al. 2022

IEEE Geosci. Remote Sens. Mag.

View full text Add to dashboard Cite

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.

show abstract

“…For example, a negative linear association of detected sentiment from Flickr data is related with people living on welfare checks. Results in [487] show that there is a high correlation between sentiment extracted from text-based social data and image-based landscape preferences by humans. In addition, results in [383] show some correlation between image and 73 http://mm.doshisha.ac.jp/senti/CrossSentiment.html textual tweets.…”

Section: Observationsmentioning

confidence: 99%

“…• Environment for policy makers [175], urban mobility [62], wind energy [211], green initiatives [487] and peatland fires [321];…”

Section: Application Areas Of Social Opinion Miningmentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

View full text Add to dashboard Cite

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

show abstract

Identification of Landscape Preferences by Using Social Media Analysis

Cited by 6 publications

References 13 publications

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Geoinformation Harvesting From Social Media Data: A community remote sensing approach

Over a Decade of Social Opinion Mining: A Systematic Review

Contact Info

Product

Resources

About