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In this work, we provide an in-depth analysis of collusive entities on YouTube fostered by various blackmarket services. Following this, we propose models to detect three types of collusive YouTube entities - videos seeking collusive likes,…
It's no secret that video has become the primary way we share information online. That's why there's been a surge in demand for algorithms that can analyze and understand video content. It's a trend going to continue as video continues to…
The educational use of video and multimedia is increasing rapidly in secondary and higher education across all disciplines. Videos for physics education can be found in many universities and other educational institutions websites all over…
Deaf and Hard-of-Hearing (DHH) audiences have long complained about caption qualities for many online videos created by individual content creators on video-sharing platforms (e.g., YouTube). However, there lack explorations of practices,…
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting the systematic exploration into video quality understanding.…
The goal of this paper is to study the behaviour of view-count in YouTube. We first propose several bio-inspired models for the evolution of the view-count of YouTube videos. We show, using a large set of empirical data, that the view-count…
Video watching had emerged as one of the most frequent media activities on the Internet. Yet, little is known about how users watch online video. Using two distinct YouTube datasets, a set of random YouTube videos crawled from the Web and a…
The digital transformation of religious practice has reshaped how billions of people engage with spiritual content, with video-sharing platforms becoming central to contemporary religious communication. Yet HCI research lacks systematic…
YouTube introduced the Shorts video format in 2021, allowing users to upload short videos that are prominently displayed on its website and app. Despite having such a large visual footprint, there are no studies to date that have looked at…
Predicting contextualised engagement in videos is a long-standing problem that has been popularly attempted by exploiting the number of views or the associated likes using different computational methods. The recent decade has seen a boom…
This paper offers a comprehensive analysis of recent advancements in video inpainting techniques, a critical subset of computer vision and artificial intelligence. As a process that restores or fills in missing or corrupted portions of…
Portraying emotion and trustworthiness is known to increase the appeal of video content. However, the causal relationship between these signals and online user engagement is not well understood. This limited understanding is partly due to a…
The proliferation of video content on platforms like YouTube and Vimeo presents significant challenges in efficiently locating relevant information. Automatic video summarization aims to address this by extracting and presenting key content…
The task of describing video content in natural language is commonly referred to as video captioning. Unlike conventional video captions, which are typically brief and widely available, long-form paragraph descriptions in natural language…
Learning assessments are subject of discussions that envolve theoretical and practical approaches. To measure learning in physics by high school students, either qualitatively or quantitatively, is a process in which it should be possible…
Video summaries or highlights are a compelling alternative for exploring and contextualizing unprecedented amounts of video material. However, the summarization process is commonly automatic, non-transparent and potentially biased towards…
Established in 2005, YouTube has become the most successful Internet site providing a new generation of short video sharing service. Today, YouTube alone comprises approximately 20% of all HTTP traffic, or nearly 10% of all traffic on the…
Text-to-video retrieval enables users to find relevant video content using natural language queries, a task that has grown increasingly important with the rapid expansion of online video. Over the past six years, research has produced…
We combine user-centric Twitter data with video-centric YouTube data to analyze who watches and shares what on YouTube. Combination of two data sets, with 87k Twitter users, 5.6mln YouTube videos and 15mln video sharing events, allows rich…
Web video is often used as a source of data in various fields of study. While specialized subsets of web video, mainly earmarked for dedicated purposes, are often analyzed in detail, there is little information available about the…