Analyzing Social Media Data for Better Understanding Students’ Learning Experiences

Ganesan, T.; Anuradha, S.; Harika, Attada; Nikitha, Neelisetty; Nalajala, Sunanda

doi:10.1007/978-981-15-9509-7_43

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…In doing so, social media is the latest phenomenon of interaction among people through virtual connected networks called social networking sites [38,39]. Social media is referred to as "computer-supported social networks" (CSSNs) [40], online social networking services [41], social networking sites (SNS) [42], social websites [43], social media platforms [44], virtual communities [45], and Web 2.0 [46]. Facebook is the most popular social media platform all over the world (except in restricted countries and territories) based on user statistics, and as of January 2021, the total number of users of Facebook was 2.740 billion, followed by YouTube (2.291 billion users) (Statistica, 2021) [47].…”

Section: Literature Review-circular Economy Social Media and Re-commercementioning

confidence: 99%

Re-Commerce to Ensure Circular Economy from Consumer Perspective

Arman

Mark‐Herbert

2021

Sustainability

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Responsible consumption practice is crucial to minimizing waste as part of sustainable development (SDG 12). This can be achieved by extending product life in a cradle-to-cradle system as part of a circular economy. However, are consumers willing to extend product life by reusing materials? The phenomenon of re-commerce, where consumers sell products to each other (C2C), takes place in physical as well as web-based markets. This project focuses on enabling factors for re-commerce practices on Facebook among consumers in Bangladesh. A review of existing literature provided grounds for an empirical focus group study of Bangladeshi consumers. Using a social practice theory perspective in a thematic analysis shows that enabling factors in terms of technical competence, context-bound conditions influencing meanings such as socially accepted procedures, and practical practices relating to materials such as payment forms and logistics support, all serve as enabling or hindering factors. The study contributes to the understanding of conditions for re-commerce practices as part of a circular economy system where consumers are encouraged to engage in responsible consumption by extending product life cycles.

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Section: Literature Review-circular Economy Social Media and Re-commercementioning

confidence: 99%

Re-Commerce to Ensure Circular Economy from Consumer Perspective

Arman

Mark‐Herbert

2021

Sustainability

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“…2021, 10, 636 2 of 16 image data. Specifically, veracity analysis of social media information [3][4][5][6][7], sentiment analysis [8][9][10][11][12], and the detection of cyberbullying [13,14] mostly use text messages exclusively. For example, Zubiaga et al [6] introduced a novel method that is able detect rumors by learning from the sequential dynamics of text reports.…”

Section: Introductionmentioning

confidence: 99%

Disaster Image Classification by Fusing Multimodal Social Media Data

Gan

Huang

Cai

et al. 2021

IJGI

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Social media datasets have been widely used in disaster assessment and management. When a disaster occurs, many users post messages in a variety of formats, e.g., image and text, on social media platforms. Useful information could be mined from these multimodal data to enable situational awareness and to support decision making during disasters. However, the multimodal data collected from social media contain a lot of irrelevant and misleading content that needs to be filtered out. Existing work has mostly used unimodal methods to classify disaster messages. In other words, these methods treated the image and textual features separately. While a few methods adopted multimodality to deal with the data, their accuracy cannot be guaranteed. This research seamlessly integrates image and text information by developing a multimodal fusion approach to identify useful disaster images collected from social media platforms. In particular, a deep learning method is used to extract the visual features from social media, and a FastText framework is then used to extract the textual features. Next, a novel data fusion model is developed to combine both visual and textual features to classify relevant disaster images. Experiments on a real-world disaster dataset, CrisisMMD, are performed, and the validation results demonstrate that the method consistently and significantly outperforms the previously published state-of-the-art work by over 3%, with a performance improvement from 84.4% to 87.6%.

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