Exploiting multimedia in creating and analysing multimedia Web archives

The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general

Young children’s food brand knowledge. Early development and associations with television viewing and parent’s diet

Brand knowledge is a prerequisite of children’s requests and choices for branded foods. We explored the development of young children’s brand knowledge of foods highly advertised on television – both healthy and less healthy. Participants were 172 children aged 3–5 years in diverse socio-economic settings, from two jurisdictions on the island of Ireland with different regulatory environments. Results indicated that food brand knowledge (i) did not differ across jurisdictions; (ii) increased significantly between 3 and 4 years; and (iii) children had significantly greater knowledge of unhealthy food brands, compared with similarly advertised healthy brands. In addition, (iv) children’s healthy food brand knowledge was not related to their television viewing, their mother’s education, or parent or child eating. However, (v) unhealthy brand knowledge was significantly related to all these factors, although only parent eating and children’s age were independent predictors. Findings indicate that effects of food marketing for un- healthy foods take place through routes other than television advertising alone, and are present before pre-schoolers develop the concept of healthy eating. Implications are that marketing restrictions of un- healthy foods should extend beyond television advertising; and that family-focused obesity prevention programmes should begin before children are 3 years of age

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

“The Other Hangover”: Implementing and Evaluating an Original, Student-Designed Campaign to Curb Binge Drinking

Binge drinking is a serious health and safety issue that has continued to plague college campuses, despite decades of education campaigns promoting moderation towards alcohol. As part of a student advertising competition, undergraduates were asked to develop an integrated marketing campaign focused on reducing binge drinking among college students which would successfully capture attention and resonate with their peers. The campaign the students created, called “The Other Hangover,” takes a unique approach to the binge-drinking issue—focusing attention on the social consequences of overconsumption, such as damage to one’s reputation and the loss of friendships. This case study examines the strategic development and implementation of the campaign, a process which was largely managed by undergraduate students connected to the project. In addition results of the evaluation effort which was conducted to measure the campaign’s success are reported, along with discussion questions designed for students and educators to further explore the relevant issues related to the project

A review of the research literature relating to ICT and attainment

Summary of the main report, which examined current research and evidence for the impact of ICT on pupil attainment and learning in school settings and the strengths and limitations of the methodologies used in the research literature

Collaborative Feature Learning from Social Media

Image feature representation plays an essential role in image recognition and related tasks. The current state-of-the-art feature learning paradigm is supervised learning from labeled data. However, this paradigm requires large-scale category labels, which limits its applicability to domains where labels are hard to obtain. In this paper, we propose a new data-driven feature learning paradigm which does not rely on category labels. Instead, we learn from user behavior data collected on social media. Concretely, we use the image relationship discovered in the latent space from the user behavior data to guide the image feature learning. We collect a large-scale image and user behavior dataset from Behance.net. The dataset consists of 1.9 million images and over 300 million view records from 1.9 million users. We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities. We also show that the learned feature performs competitively on various recognition benchmarks