12 research outputs found
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1
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The Medium and the Backlash: The Disparagement of the #MeToo Movement in Online Public Discourse in South Korea
This study examines the #MeToo movement in South Korea to understand the role of online platforms in the development of a backlash discourse. We apply computational methods to analyze how the #MeToo movement was discussed by citizens on Twitter and in online news comments, in contrast to the traditional news media. Our findings show that the public discourse in user-driven online platforms enabled the proliferation of a disparaging narrative that challenged the movement, while the patterns of the backlash differed across platforms. Using word-embedding techniques and network analyses, we illustrate the shift in frames around #MeToo movement and highlight how platform affordances meaningfully shaped the way the backlash unfolded
SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration
The potential social harms that large language models pose, such as
generating offensive content and reinforcing biases, are steeply rising.
Existing works focus on coping with this concern while interacting with
ill-intentioned users, such as those who explicitly make hate speech or elicit
harmful responses. However, discussions on sensitive issues can become toxic
even if the users are well-intentioned. For safer models in such scenarios, we
present the Sensitive Questions and Acceptable Response (SQuARe) dataset, a
large-scale Korean dataset of 49k sensitive questions with 42k acceptable and
46k non-acceptable responses. The dataset was constructed leveraging HyperCLOVA
in a human-in-the-loop manner based on real news headlines. Experiments show
that acceptable response generation significantly improves for HyperCLOVA and
GPT-3, demonstrating the efficacy of this dataset.Comment: 19 pages, 10 figures, ACL 202
IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland.
This Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6) summarises the state of knowledge of climate change,
its widespread impacts and risks, and climate change mitigation and adaptation. It integrates the main findings of the Sixth
Assessment Report (AR6) based on contributions from the three Working Groups1
, and the three Special Reports. The summary for Policymakers (SPM) is structured in three parts: SPM.A Current Status and Trends, SPM.B Future Climate Change, Risks, and
Long-Term Responses, and SPM.C Responses in the Near Term.This report recognizes the interdependence of climate, ecosystems and biodiversity, and human societies; the value of diverse forms of knowledge; and the close linkages between climate change adaptation, mitigation, ecosystem health, human well-being
and sustainable development, and reflects the increasing diversity of actors involved in climate action.
Based on scientific understanding, key findings can be formulated as statements of fact or associated with an assessed level of
confidence using the IPCC calibrated language
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of 24,752 labeled images on fashion conversations, containing visual and textual cues, available for the research community
Fashion conversation data on Instagram
Our fashion dataset is composed of information about 24,752 posts by 13,350 people on Instagram. The data collection was done over a month period in January, 2015. We searched for posts mentioning 48 internationally renowned fashion brand names as hashtag. Our data contain information about hashtags as well as image features based on deep learning (Convolutional Neural Network or CNN). The list of learned features include selfies, body snaps, marketing shots, non-fashion, faces, logo, etc. Please refer to our paper for the full description of how we built our deep learning model
Characterizing Clickbaits on Instagram
Clickbaits are routinely utilized by online publishers to attract the attention of people in competitive media markets. Clickbaits are increasingly used in visual-centric social media but remain a largely unexplored problem. Existing defense mechanisms rely on text-based features and are thus inapplicable to visual social media. By exploring the relationships between images and text, we develop a novel approach to characterize clickbaits on visual social media. Focusing on the topic of fashion, we first examined the prevalence of clickbaits on Instagram and surveyed their negative impacts on user experience through a focus group study (N=31). In a largescale analysis, we collected 450,000 Instagram posts and manually labeled 12,659 of these posts to determine what people consider to be clickbaits. By combining three different types of features (e.g., image, text, and meta features), our classifier was able detect clickbaits with an accuracy of 0.863. We performed an extensive feature analysis and showed that content-based features are much more important than meta features (e.g., number of followers) in clickbait classification. Our analysis indicates that approximately 11% of fashion-related Instagram posts are clickbait and that these posts are consistently accompanied by many hashtags, thus demonstrating that clickbait is prevalent in visual social media
Automatically Detecting Image–Text Mismatch on Instagram with Deep Learning
© 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.Visual social media have emerged as an essential brand communication channel for advertisers and brands. The active use of hashtags has enabled advertisers to identify customers interested in their brands and better understand their consumers. However, some users post brand-incongruent content—for example, posts composed of brand-irrelevant images with brand-relevant hashtags. Such visual information mismatch can be problematic because it hinders other consumers’ information search processes and advertisers’ insight generation from consumer-initiated social media data. This study aims to characterize visually mismatched content in brand-related posts on Instagram and builds a visual information mismatch detection model using computer vision. We propose a machine-learning model based on three cues: image, text, and metadata. Our analysis shows the effectiveness of deep learning and the importance of combining text and image features for mismatch detection. We discuss the advantages of machine-learning methods as a novel research tool for advertising research and conclude with implications of our findings.11Nssciscopu
Clickbaits Labeling Data on Instagram
Our dataset is composed of information about 7,769 posts on Instagram. The data collection was done over a two-week period in July 2017 using an InstaLooter API. We searched for posts mentioning 62 internationally renowned fashion brand names as hashtag
Public Discourse on Environmental Pollution and Health in Korea: Tweets Following the Fukushima Nuclear Accident
Public discourse on environmental and health issues has risenon social media. Upon an environmental crisis, various chatterssuch as breaking news, misinformation, and rumor couldaggravate social confusion and proliferate negative publicsentiment. In an effort to study public sentiments on environmentalissues in South Korea, we analyzed 158,964 tweetsgenerated over a 4-year period following the Fukushima accidentin 2011, the largest release of radioactivity to environmentin recent history. This event led to a significant increasein public’s interest on environmental and nuclear issues inKorea. We employed Bayesian network and recursive partitioningto observe the classification regression tree structureof major topics. Topics on health and environment were interlinkedclosely and represented both apprehension and concernabout health threats and pollution. Our methodologyhelps analyze large online discourse efficiently and offers insightto crisis response organizations