Influencer Videos: Unboxing the Mystique
- URL: http://arxiv.org/abs/2012.12311v3
- Date: Tue, 21 Nov 2023 15:40:54 GMT
- Title: Influencer Videos: Unboxing the Mystique
- Authors: Prashant Rajaram and Puneet Manchanda
- Abstract summary: We study YouTube influencers and analyze their unstructured video data across text, audio and images.
Our prediction-based approach analyzes unstructured data and finds that "what is said" in words (text) is more influential than "how it is said" in imagery (images) or acoustics (audio)
We uncover novel findings that establish distinct associations for measures of shallow and deep engagement based on the dual-system framework of human thinking.
- Score: 0.4143603294943439
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Influencer marketing has become a very popular tool to reach customers.
Despite the rapid growth in influencer videos, there has been little research
on the effectiveness of their constituent features in explaining video
engagement. We study YouTube influencers and analyze their unstructured video
data across text, audio and images using an "interpretable deep learning"
framework that accomplishes both goals of prediction and interpretation. Our
prediction-based approach analyzes unstructured data and finds that "what is
said" in words (text) is more influential than "how it is said" in imagery
(images) or acoustics (audio). Our novel interpretation-based approach is
implemented after completion of model prediction by analyzing the same source
of unstructured data to measure importance attributed to the video features. We
eliminate several spurious relationships in two steps, identifying a subset of
relationships which are confirmed using theory. We uncover novel findings that
establish distinct associations for measures of shallow and deep engagement
based on the dual-system framework of human thinking. Our approach is validated
using simulated data, and we discuss the learnings from our findings for
influencers and brands.
Related papers
- Compositional Video Generation as Flow Equalization [72.88137795439407]
Large-scale Text-to-Video (T2V) diffusion models have recently demonstrated unprecedented capability to transform natural language descriptions into stunning and photorealistic videos.
Despite the promising results, these models struggle to fully grasp complex compositional interactions between multiple concepts and actions.
We introduce bftextVico, a generic framework for compositional video generation that explicitly ensures all concepts are represented properly.
arXiv Detail & Related papers (2024-06-10T16:27:47Z) - How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios [73.24092762346095]
We introduce two large-scale datasets with over 60,000 videos annotated for emotional response and subjective wellbeing.
The Video Cognitive Empathy dataset contains annotations for distributions of fine-grained emotional responses, allowing models to gain a detailed understanding of affective states.
The Video to Valence dataset contains annotations of relative pleasantness between videos, which enables predicting a continuous spectrum of wellbeing.
arXiv Detail & Related papers (2022-10-18T17:58:25Z) - Discourse Analysis for Evaluating Coherence in Video Paragraph Captions [99.37090317971312]
We are exploring a novel discourse based framework to evaluate the coherence of video paragraphs.
Central to our approach is the discourse representation of videos, which helps in modeling coherence of paragraphs conditioned on coherence of videos.
Our experiment results have shown that the proposed framework evaluates coherence of video paragraphs significantly better than all the baseline methods.
arXiv Detail & Related papers (2022-01-17T04:23:08Z) - Interventional Video Grounding with Dual Contrastive Learning [16.0734337895897]
Video grounding aims to localize a moment from an untrimmed video for a given textual query.
We propose a novel paradigm from the perspective of causal inference to uncover the causality behind the model and data.
We also introduce a dual contrastive learning approach to better align the text and video.
arXiv Detail & Related papers (2021-06-21T12:11:28Z) - Video Sentiment Analysis with Bimodal Information-augmented Multi-Head
Attention [7.997124140597719]
This study focuses on the sentiment analysis of videos containing time series data of multiple modalities.
The key problem is how to fuse these heterogeneous data.
Based on bimodal interaction, more important bimodal features are assigned larger weights.
arXiv Detail & Related papers (2021-03-03T12:30:11Z) - Video SemNet: Memory-Augmented Video Semantic Network [14.64546899992196]
We propose a machine learning approach to capture the narrative elements in movies by bridging the gap between the low-level data representations and semantic aspects of the visual medium.
We present a Memory-Augmented Video Semantic Network, called Video SemNet, to encode the semantic descriptors and learn an embedding for the video.
We demonstrate that our model is able to predict genres and IMDB ratings with a weighted F-1 score of 0.72 and 0.63 respectively.
arXiv Detail & Related papers (2020-11-22T01:36:37Z) - Neuro-Symbolic Representations for Video Captioning: A Case for
Leveraging Inductive Biases for Vision and Language [148.0843278195794]
We propose a new model architecture for learning multi-modal neuro-symbolic representations for video captioning.
Our approach uses a dictionary learning-based method of learning relations between videos and their paired text descriptions.
arXiv Detail & Related papers (2020-11-18T20:21:19Z) - Content-based Analysis of the Cultural Differences between TikTok and
Douyin [95.32409577885645]
Short-form video social media shifts away from the traditional media paradigm by telling the audience a dynamic story to attract their attention.
In particular, different combinations of everyday objects can be employed to represent a unique scene that is both interesting and understandable.
Offered by the same company, TikTok and Douyin are popular examples of such new media that has become popular in recent years.
The hypothesis that they express cultural differences together with media fashion and social idiosyncrasy is the primary target of our research.
arXiv Detail & Related papers (2020-11-03T01:47:49Z) - Learning from Context or Names? An Empirical Study on Neural Relation
Extraction [112.06614505580501]
We study the effect of two main information sources in text: textual context and entity mentions (names)
We propose an entity-masked contrastive pre-training framework for relation extraction (RE)
Our framework can improve the effectiveness and robustness of neural models in different RE scenarios.
arXiv Detail & Related papers (2020-10-05T11:21:59Z) - How-to Present News on Social Media: A Causal Analysis of Editing News
Headlines for Boosting User Engagement [14.829079057399838]
We analyze media outlets' current practices using a data-driven approach.
We build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared.
Then, we explore how those media edited tweets against original headlines and the effects of such changes.
arXiv Detail & Related papers (2020-09-17T06:39:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.