Growing Media Skills and Know-How In Situ: Technology-Enhanced Practices and Collaborative Support in Mobile News-Reporting

Abstract

Over the past decade, mobile news production has had a growing prevalence and has been established as a new type by modern journalism industry. Journalists understand content capturing and sharing as parts of their role in newsrooms. Mobile journalism (mojo) is an evolving form of reporting in which where people use only a smartphone to create and file stories, and it has been gaining ground during the last decade. This paper aims to examine the difficulties, issues, and challenges in real-world mojo scenarios, analyzing the efficacy of prototype machine-assisted reporting services (MoJo-MATE). A usability evaluation is conducted in quantitative and qualitative terms, paying attention to the media literacy support provided through implemented tools and the proposed collaborations. Students of the School of Journalism and Mass Communications, along with postgraduate-level researchers and professional journalists, form the sample for this investigation, which has a two-folded target: To guide the rapid prototyping process for system development and to validate specific hypotheses by answering the corresponding research questions. The results indicate the impact of mobile/on-demand support and training on journalistic practices and the attitudes of future journalists towards specialized technology in the era of constantly evolving digital journalism.

Semantic Crowdsourcing of Soundscapes Heritage: A Mojo Model for Data-Driven Storytelling

“…These processes feature many resemblances with aspects of the MoJo paradigm and other Digital Journalism genres (i.e., Data, Multimedia, Immersive Journalism, etc.) [16][17][18][19][20][21][22][23][24]48]. Context-and location-aware services can be combined with (multichannel) semantic processing to offer spatiotemporal sound mapping and pattern-related visualizations.…”

“…In this view, crowdsourced audio data can offer soundscape enhancement with multiple augmentation layers in favor of documentation, data-driven storytelling, and management. The massive research progress on the domain has established multiple pattern recognition schemes and hierarchical semantic audio taxonomies to describe the sound-fields associated with the different social events [13][14][15][16][17][18][19][20][21][22][23][24][52][53][54][55][56][57][58][59]. Apart from the geographical-and time-related information that a mobile terminal can easily hold, environmental sounds and soundscapes can be classified, filtered, and highlighted based on the associated pattern classification taxonomies, various low-level audio descriptors, other semantic labels concerning the transmitted or perceived emotions, etc.…”

“…For this reason, early backend implementations for General Audio Classification and Detection are presented and evaluated. The successful implementation of MoJo-mate, a mobile application offering machine-assisted reporting with semantically enhanced capture and documentation MoJo facilities [23,24], justifies this approach. The encompassed audio processing and recognition layers exhibit stateof-the-art time-, context-and location-aware ubiquitous computing services, combined with generic/hierarchical pattern classification schemes [13][14][15][16].…”

“…The current work focuses on the collaborative collection and documentation of soundscapes and environmental sound semantics, which apart from CH, also significantly impact human life quality in multiple perspectives (as explained in the next sections). The whole approach has many similarities with sophisticated Mobile Journalism (MoJo) services, helping professional and citizen journalists collect news-items and shape them into featured data-driven storytelling [23,24]. Relying on the so-called MoJo-mate platform (Mobile Journalism Machine-Assisted Reporting) [23,24], an analysis is held regarding model elaboration and adaptation for the needs of soundscape heritage purposes.…”

“…The whole approach has many similarities with sophisticated Mobile Journalism (MoJo) services, helping professional and citizen journalists collect news-items and shape them into featured data-driven storytelling [23,24]. Relying on the so-called MoJo-mate platform (Mobile Journalism Machine-Assisted Reporting) [23,24], an analysis is held regarding model elaboration and adaptation for the needs of soundscape heritage purposes. In this perspective, state-of-the-art machine and deep learning services are implemented both client-(mobile) and cloud-wise.…”