Mining Social Media for Newsgathering: A Review

Social media is becoming an increasingly important data source for learning about breaking news and for following the latest developments of ongoing news. This is in part possible thanks to the existence of mobile devices, which allows anyone with access to the Internet to post updates from anywhere, leading in turn to a growing presence of citizen journalism. Consequently, social media has become a go-to resource for journalists during the process of newsgathering. Use of social media for newsgathering is however challenging, and suitable tools are needed in order to facilitate access to useful information for reporting. In this paper, we provide an overview of research in data mining and natural language processing for mining social media for newsgathering. We discuss five different areas that researchers have worked on to mitigate the challenges inherent to social media newsgathering: news discovery, curation of news, validation and verification of content, newsgathering dashboards, and other tasks. We outline the progress made so far in the field, summarise the current challenges as well as discuss future directions in the use of computational journalism to assist with social media newsgathering. This review is relevant to computer scientists researching news in social media as well as for interdisciplinary researchers interested in the intersection of computer science and journalism.Comment: Accepted for publication in Online Social Networks and Medi

Learning efficient temporal information in deep networks: From the viewpoints of applications and modeling

With the introduction of deep learning, machine learning has dominated several technology areas, giving birth to high-performance applications that can even challenge human-level accuracy. However, the complexity of deep models is also exploding as a by-product of the revolution of machine learning. Such enormous model complexity has raised the new challenge of improving the efficiency in deep models to reduce deployment expense, especially for systems with high throughput demands or devices with limited power. The dissertation aims to improve the efficiency of temporal-sensitive deep models in four different directions. First, we develop a bandwidth extension mapping to avoid deploying multiple speech recognition systems corresponding to wideband and narrowband signals. Second, we apply a multi-modality approach to compensate for the performance of an excitement scoring system, where the input video sequences are aggressively down-sampled to reduce throughput. Third, we formulate the motion feature in the feature space by directly inducing the temporal information from intermediate layers of deep networks instead of relying on an additional optical flow stream. Finally, we model a spatiotemporal sampling network inspired by the human visual perception mechanism to reduce input frames and regions adaptively

Going for GOAL: A Resource for Grounded Football Commentaries

Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both of these properties can lead to spurious cues to be exploited by models rather than learning to ground language. In this paper, we present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or `soccer') highlights videos with transcribed live commentaries in English. As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding. We also provide state-of-the-art baselines for the following tasks: frame reordering, moment retrieval, live commentary retrieval and play-by-play live commentary generation. Results show that SOTA models perform reasonably well in most tasks. We discuss the implications of these results and suggest new tasks for which GOAL can be used. Our codebase is available at: https://gitlab.com/grounded-sport-convai/goal-baselines.Comment: Preprint formatted using the ACM Multimedia template (8 pages + appendix

Giants with feet of clay: the sustainability of the business models in music streaming services

This paper examines the sustainability of the recorded music industry from the perspective of music performers. Music streaming platforms, or digital music service providers (DMSPs), have changed the recorded music industry paradigm since the middle of the 2010s. Business models for performers have evolved from royalty agreements based on sales to more complex remuneration systems based on revenues from a combination of (ad-based) free and paid subscriptions. Previous research has mainly focused on the examination of the business models of streaming services from the point of view of the innovation players (digital platforms) and/or the traditional dominant intermediaries (record labels and publishers). However, not all innovation-driven transformations are sustainable. In this paper, we argue that the sustainability of the main business models in the music industry demands the consideration of the performers’ perspective. We combine a qualitative approach with primary and secondary data sources to investigate the sustainability of existing trends of business models and business practices for different categories of performers, including both monetary values and a description of how revenues are shared. We conclude that DMSPs foster an asymmetric value chain in which the creative players barely capture value while technology-based innovations increase the capability of DMSPs to generate and capture value. Finally, we outline some alternative business models looking for the long-term sustainability of the digital music marketplace

Multimodal framework based on audio‐visual features for summarisation of cricket videos

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166171/1/ipr2bf02094.pd

The Dynamics of Influencer Marketing

YouTube, Instagram, Facebook, Vimeo, Twitter, etc. have their own logics, dynamics and different audiences. This book analyses how the users of these social networks, especially those of YouTube and Instagram, become content prescribers, opinion leaders and, by extension, people of influence. What influence capacity do they have? Why are intimate or personal aspects shared with unknown people? Who are the big beneficiaries? How much is vanity and how much altruism? What business is behind these social networks? What dangers do they contain? What volume of business can we estimate they generate? How are they transforming cultural industries? What legislation is applied? How does the legislation affect these communications when they are sponsored? Is the privacy of users violated with the data obtained? Who is the owner of the content? Are they to blame for ""fake news""? In this changing, challenging and intriguing environment, The Dynamics of Influencer Marketing discusses all of these questions and more. Considering this complexity from different perspectives: technological, economic, sociological, psychological and legal, the book combines the visions of several experts from the academic world and provides a structured framework with a wide approach to understand the new era of influencing, including the dark sides of it. It will be of direct interest to marketing scholars and researchers while also relevant to many other areas affected by the phenomenon of social media influence