Difference between revisions of "Be The Change: Video Analysis for Environmental Sustainability"
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{{StudentProjectTemplate | {{StudentProjectTemplate | ||
| − | |Summary= | + | |Summary=Analysis of online climate change video contents and identification of video features rendering a video ‘effective’ using machine learning techniques |
|Keywords=video analysis, deep learning, representation learning, climate change | |Keywords=video analysis, deep learning, representation learning, climate change | ||
|TimeFrame=Fall 2022 | |TimeFrame=Fall 2022 | ||
Revision as of 11:12, 3 October 2022
| Title | Be The Change: Video Analysis for Environmental Sustainability |
|---|---|
| Summary | Analysis of online climate change video contents and identification of video features rendering a video ‘effective’ using machine learning techniques |
| Keywords | video analysis, deep learning, representation learning, climate change |
| TimeFrame | Fall 2022 |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Ece Calikus, Prayag Tiwari, Sławomir Nowaczyk |
| Level | Master |
| Status | Open |
Climate change is the greatest global threat facing the world in the 21st century, according to organizations such as the UN & WHO. In the fight against climate change, one of the key goals is awareness-raising about the environmental challenges & actions that can be taken to mitigate them. Education is key to addressing climate change (Meyer, 2015). Commission calls for env. sustainability to be at the core of EU education & training systems. Despite the problem's urgency, there is a lack of educational programs on climate change in the context of formal education. In Europe, in particular, there is a lack of innovative and engaging learning environments to foster scientific literacy. Our project aims to develop an Interactive Educational Programme (IEP) on climate change, which transforms both challenges into advantages. By harnessing social media video education on climate change, we aim to perform automated & structured analytics on both the contents & users’ reactions in order to identify the elements that make a video on climate change appealing and engaging.
This project specifically focuses on two goals: (a) analyzing online video features and identification of video features rendering a video ‘effective’ using machine learning techniques and (b) analyzing video chat content and identification of commentary characteristics of ‘effective’ videos.
