Bahareh Rahmanzadeh Heravi

Twitter journalism in Ireland: sourcing and trust in the age of social media*

ABSTRACT Twitter has been widely adopted into journalistic workflows, as it provides instant and widespread access to a plethora of content about breaking news events, while also serving to disseminate reporting on those events. The content on Twitter, however, poses several challenges for journalists, as it arrives unfiltered, full of noise, and at an alarming velocity. Building on the results of the first national survey of social media use in Irish newsrooms, this paper investigates the adoption of social media into journalistic workflows, journalists’ attitudes towards various aspects of social media, and the content and perspectives generated by these online communities. It particularly investigates how Twitter shapes the processes of sourcing and verification in newsrooms, and assesses how notions of trust factor into the adoption of the Twitter platform and content into these processes. The paper further analyses relationships between journalist profile and adopted practices and attitudes, and seeks to understand how Twitter operates in the current journalistic landscape. While this paper draws its data from a survey of journalists in Ireland, the analysis of the relationship between trust, sourcing, and verification reveals broader patterns about journalistic values, and how these values and practices may operate in the field of journalism as a whole.

Towards an Ontology for Automating Collaborative Business Processes

Automation of business transactions between trading partners is an important factor in today’s global business. XML based e-Business standards are developed to provide a shared understanding on what information to share, when and how between trading partners. However these standards can only capture the syntax of the transactions and not the semantics. This paper presents an ontology for ebXML Business Process Specification Schema (ebBP), with the aim of empowering the capture and sharing semantics embedded within B2B processes, enabling knowledge deduction and reasoning over this shared knowledge. The ebBP ontology presented covers both syntax included in the ebBP XML schema and the informal semantics of the ebBP specification and is fundamentally different from an automatic transformation of XML to OWL. This ontology is evaluated against a set of competency questions, using a publicly available ordering process. This paper demonstrates how semantic web technologies can be utilised in order to improve standards-based interoperability between trading partners.

Where Is the News Breaking? Towards a Location-Based Event Detection Framework for Journalists

The rise of user-generated content (UCG) as a source of information in the journalistic lifecycle is driving the need for automated methods to detect, filter, contextualise and verify citizen reports of breaking news events. In this position paper we outline the technological challenges in incorporating UCG into news reporting and describe our proposed framework for exploiting UGC from social media for location-based event detection and filtering to reduce the workload of journalists covering breaking and ongoing news events. News organisations increasingly rely on manually curated UGC. Manual monitoring, filtering, verification and curation of UGC, however, is a time and effort consuming task, and our proposed framework takes a first step in addressing many of the issues surrounding these processes.

Topic Detection in Twitter Using Topology Data Analysis

The massive volume of content generated by social media greatly exceeds human capacity to manually process this data in order to identify topics of interest. As a solution, various automated topic detection approaches have been proposed, most of which are based on document clustering and burst detection. These approaches normally represent textual features in standard n-dimensional Euclidean metric spaces. However, in these cases, directly filtering noisy documents is challenging for topic detection. Instead we propose Topol, a topic detection method based on Topology Data Analysis (TDA) that transforms the Euclidean feature space into a topological space where the shapes of noisy irrelevant documents are much easier to distinguish from topically-relevant documents. This topological space is organised in a network according to the connectivity of the points, i.e. the documents, and by only filtering based on the size of the connected components we obtain competitive results compared to other state of the art topic detection methods.

Ontology-based standards development: Application of OntoStanD to ebXML business process specification schema

Business-to-Business (B2B) interoperations are an important part of todays global economy. Business process standards are developed to provide a common understanding of the information shared between trading partners. These standards, however, mainly capture the syntax of the transactions and not their semantics. This paper proposes the use of ontologies as the basis for standards development and presents an ontology for the ebXML Business Process Specification Schema (ebBP) with the aim of empowering the capture and sharing of semantics embedded within B2B processes as well as enabling knowledge deduction and reasoning over the shared knowledge. The paper utilises the Ontology-based Standards Development methodology (OntoStanD) as a methodological approach for designing ontological models of standards. This research demonstrates how Semantic Web technologies can be utilised as a basis for standards development and representation in order to improve standards-based interoperability between trading partners.

Patterns in Award Winning Data Storytelling: Story Types, Enabling Tools and Competences

Data storytelling is rapidly gaining prominence as a characteristic activity of digital journalism with significant adoption by small and large media houses. While a handful of previous studies have examined what characterises aspects of data storytelling like narratives and visualisation or analysis based on single cases, we are yet to see a systematic effort to harness these available resources to gain better insight into what characterises good data stories and how these are created. This study analysed 44 cases of outstanding data storytelling practices comprising winning entries of the Global Editors Network’s Data Journalism Award from 2013 to 2016 to bridge this knowledge gap. Based on a conceptual model we developed, we uniformly characterised each of the 44 cases and then proceeded to determine types of these stories and the nature of technologies employed in creating them. Our findings refine the traditional typology of data stories from the journalistic perspective and also identify core technologies and tools that appear central to good data journalism practice. We also discuss our findings in relations to the recently published 2017 winning entries. Our results have significant implications for the required competencies for data journalists in contemporary and future newsrooms.

Sourcing and trust: Twitter journalism in Ireland

Social media, in particular Twitter, have been widely adopted in newsrooms for various purposes, including sourcing news leads and content, disseminating stories, soliciting user comments and driving traffic to corporate websites. This paper investigates the ways in which journalists use social media for sourcing and verification, and their attitudes towards social media in terms of trust. The analysis is built on a survey of journalists in Ireland conducted in 2013, which revealed that journalists in Ireland are heavy adopters of Twitter in their workflows, and in particular use social media for sourcing news leads and content. However, they are highly skeptical about the level of trust in social media. While this paper focuses on journalists in Ireland, the analysis of the relationship between trust, sourcing and verification reveals broader patterns about journalistic values, and how these values and practices operate in the new media landscape.

Towards Social Event Detection and Contextualisation for Journalists

Social media platforms have become an important source of information in course of a breaking news event, such as natural calamity, political uproar, etc. News organisations and journalists are increasingly realising the value of information being propagated via social media. However, the sheer volume of the data produced on social media is overwhelming and manual inspection of this streaming data for finding, aggregation, and contextualising emerging event in a short time span is a day-to-day challenge by journalists and media organisations. It highlights the need for better tools and methods to help them utilise this user generated information for news production. This paper addresses the above problem for journalists by proposing an event detection and contextualisation framework that receives an input stream of social media data and generates the likely events in the form of clusters along with a certain context.

3WS of Data Journalism Education: What, where and who?

This paper explores data journalism education, with a particular focus on formal training in the higher education sector globally. The study draws on data from: (1) the 2017 Global Data Journalism Survey, to study the state of data journalism education and the requirements in terms of training and (2) a dataset of 219 unique modules or programmes on data journalism or related fields that were curated and examined in order to understand the nature of data journalism education in universities across the world. The results show that while journalists interested in data are highly educated in journalism or closely related fields, they do not have a strong level of education in the more technical areas of data journalism, such as data analysis, coding and data visualisation. The study further reveals that a high proportion of data journalism courses are concentrated in the United States, with a growing number of courses developing across the world, and particularly in Europe. Despite this, education in the field does not have a strong academic underpinning, and while many courses are emerging in this area, there are not enough academically trained instructors to lead and/or teach such interdisciplinary programmes in the higher education sector.

Data Journalism in 2017: A Summary of Results from the Global Data Journalism Survey

Data journalism is an emerging discipline, which as a practice it is rapidly becoming an integral part of many newsrooms. Despite this growth, there is a lack of systematic research in this area to reveal the best practices, knowledge sets, and skills required to develop the discipline. To address this gap, this paper presents a brief overview of the results of the first Global Data Journalism Survey, which includes the participation of journalists from 43 countries. Presented results shed light on a variety of aspects of data journalism practice across the globe, including demographics, skills, education, and formation of data teams, as well as the opportunities and values associated with data journalism.