Analysis and Forecasting of Trending Topics in Online Media Streams

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201

The ‘danmu’ phenomenon and media participation: Intercultural understanding and language learning through ‘The Ministry of Time’

While research on Western multimedia platforms, such as YouTube, is prolific and interdisciplinary, Asian portals remain unknown. We explore this field by analyzing the juvenile and intercultural uses of a popular visualization system in Japan and China, known as “danmaku” or “danmu”. This technology inserts dynamic and contextualized comments on a photogram, with several typographical possibilities. Based on a corpus of 1,590 comments on “The Ministry of Time,” collected from a fandom platform with millions of users, we analyzed the topics that arouse the most interest among Chinese fans. We combine content analysis, which incorporates coding and counting techniques of the categories with the most interventions (n>16), with multimodal discourse analysis (TV series, Asian platform and user comments). Results show that the viewers are most interested in the film genre (time travel), the characters, the plot, certain sociocultural contents, and the Spanish language. Their discussions address issues of interculturality, some topics that are taboo in China and the fandom culture in Asia. Our study illustrates the potential of participation, communication, and learning in Asian social media, and constitutes an interesting and innovative contribution to the field of media and digital literacy, with various suggestions to promote intercultural competence with the use of popular culture

A Broad Evaluation of the Tor English Content Ecosystem

Tor is among most well-known dark net in the world. It has noble uses, including as a platform for free speech and information dissemination under the guise of true anonymity, but may be culturally better known as a conduit for criminal activity and as a platform to market illicit goods and data. Past studies on the content of Tor support this notion, but were carried out by targeting popular domains likely to contain illicit content. A survey of past studies may thus not yield a complete evaluation of the content and use of Tor. This work addresses this gap by presenting a broad evaluation of the content of the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web and, through topic and network analysis, characterize the types of information and services hosted across a broad swath of Tor domains and their hyperlink relational structure. We recover nine domain types defined by the information or service they host and, among other findings, unveil how some types of domains intentionally silo themselves from the rest of Tor. We also present measurements that (regrettably) suggest how marketplaces of illegal drugs and services do emerge as the dominant type of Tor domain. Our study is the product of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a collection of over 150,000 Tor pages. We make a dataset of the intend to make the domain structure publicly available as a dataset at https://github.com/wsu-wacs/TorEnglishContent.Comment: 11 page

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

Harnessing the creativity of digital multimedia tools in distance learning

Over the past few decades, advances in information and communication technologies, and particularly the digitisation of information, have brought about radical changes in the way media can be produced, distributed and shared. The exchange of information, once predominately the domain of the written word, now also embraces the digital technologies of audio and video. User-generated multimedia content proliferates, and the presence of audio and video adds dimensions that greatly increase the amount of information an audience can assimilate, adding a richness and depth to the messages we want to convey. This paper presents and discusses a creative approach to the use of digital multimedia production tools incorporated in the Open University’s 60 credit level 2 module, T215 Communication and Information Technologies. These tools are used in a way that: - explores new ways to help people understand technical concepts; - supports the development of students’ technical skills; - provides opportunities for students to be creative; - provides an alternative to traditional text-based assessment. We briefly explain the key decisions made by the module team during the design stages of the teaching materials and explain the common assessment framework used throughout the different blocks of the module. We then draw on the experience of two presentations of the module, each attracting around 500 students, to examine how students have engaged with the video creation activities and to identify issues that arise in supporting students for these tasks in a distance learning environment. Finally we discuss the success of the assessment task: a 30-second video designed to explain a technical concept related to one of the module topics

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin