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Towards a Big Data Platform
for News Angles?
Marc Gallofr´e Oca˜na, Lars Nyre, Andreas L. Opdahl,
Bjørnar Tessem, Christoph Trattner, and Csaba Veres
Dept. of Information Science and Media Studies, University of Bergen, Norway
{Marc.Gallofre,Lars.Nyre,Andreas.Opdahl,Bjornar.Tessem,
Christoph.Trattner,Csaba.Veres}@uib.no
http://www.uib.no/en/rg/ssis
Abstract Finding good angles on news events is a central journalistic
and editorial skill. As news work becomes increasingly computer-assisted
and big-data based, journalistic tools therefore need to become better
able to support news angles too. This paper outlines a big-data platform
that is able to suggest appropriate angles on news events to journalists.
We first clarify and discuss the central characteristics of news angles.
We then proceed to outline a big-data architecture that can propose
news angles. Important areas for further work include: representing news
angles formally; identifying interesting and unexpected angles on unfold-
ing events; and designing a big-data architecture that works on a global
scale.
Keywords: Big data ·Journalistic tools ·News ·Semantic technologies.
1 Introduction
Journalistic work is becoming increasingly reliant on computers and the inter-
net [20]. Miroshnichenko [25] argues strongly for artificial intelligence (AI) in
journalism and points to four areas of impact: data mining, topic selection,
commentary moderation, and news writing. Journalistic robots developed by
commercial companies such as Narrative Science and Automated Insights can
already generate news stories in areas like finance and sports automatically [18].
According to [25], Automated Insight’s Wordsmith tool wrote and published 1.5
billion news stories in 2016 alone, possibly more than all the human journalists
in the world combined.
These developments in AI are driven in part by the availability of big and
open data sources that are relevant for journalism. For example, researchers have
investigated how news events can be extracted from big-data sources such as
Tweets [15] and other texts [13]. Maiden et al. [21] propose the INJECT tool to
?Supported by the Norwegian Research Council IKTPLUSS project 275872 News
Angler, which is a collaboration with Wolftech AB, Bergen, Norway.
Copyright held by the authors. NOBIDS 2018
2 M. Gallofr´e Oca˜na et al.
support journalistic creativity during the early phases of news work. Their tool
suggests relevant news stories to trigger new ideas for story angles more quickly
and efficiently, and it has been tested in Norwegian and German newspapers.
Researchers have also used semantic technologies, such as RDF and OWL [1],
to make big and open data sources more readily available for journalistic pur-
poses [32] — and for journalistic AI tools [9]. Fernandez et al. [6] propose an
ontology for streamlining news production and distribution processes. Heravi
et al. [12] advocate social semantic journalism, which uses natural-language
processing (NLP) and semantic metadata together to detect news events from
socially-generated big data, verify information and its sources, identify eyewit-
nesses, and contextualise news events and their coverage. Leban et al. [19] present
a platform that collects news messages and lifts them into a semantic knowledge
graph (in RDF) in order to detect and describe news events in real time. In
collaboration with Wolftech AB, a software company that delivers newsroom
systems to the international market, our research group has developed News
Hunter, an architecture and proof-of-concept prototype that supports journal-
ists and other news professionals by building and mining semantic knowledge
graphs that represent news-related information [27,3] (see Section 3).
All newsworthy events have remarkable qualities whether or not journalists
are aware of them. Certain events are so remarkable that no effort is needed
to find the best angle, whereas other events need to be probed, explored, and
criticised to identify an angle that will interest the readers (or listeners, viewers).
Finding good angles on news events thus resembles topic selection [25], but
also is technique for presenting news stories in interesting ways. The task has
traditionally been the responsibility of professional journalists [30, p. 115] and is
considered a journalistic “trick-of-the-trade”. It is covered in most introductory
textbooks, but appears to have been little theorised in the research literature.
As news work becomes increasingly computer-assisted and big-data based,
journalistic tools must become better able to identify and propose suitable angles
too. This paper therefore investigates whether and how our News Hunter archi-
tecture and tool can be evolved to handle big data and extended to provide
support for news angles. We ask: Which characteristics of news angles need to
be captured and represented for them to be supported by journalistic tools? and
What are the open research issues related to creating a big-data platform that
supports news angles? Our aim is not to automate, but to support: we want to
aid journalists by detecting new events and by suggesting newsworthy angles on
them, along with relevant background information.
To investigate these questions, the rest of the paper is organised as follows:
Section 2 first clarifies and discusses the central characteristics of news angles
and related terms. Section 3 proceeds to describe News Hunter, our evolving
big-data architecture and tool for journalistic work. Finally, Section 4 concludes
the paper by reviewing open research issues.
Towards a Big Data Platform for News Angles 3
Table 1. Alternative angles on the same football event.
Event: Football team A beats team B 2–0 in city C on date D.
Impact: “Historically important team B is now relegated and on the
brink of bankruptcy.”
Influence: “The results of team A correlate with civil unrest and domestic
violence in their home town.”
Conflict: “Coach A publicly insults rival coach B!”
Conflict: “Supporters of these two teams have been fighting in the past.”
Recency: “Join our feed for live results.”
Actionability: “Join our newspaper’s campaign to get rid of coach B!”
Proximity: “Goalkeeper B grew up down the street from our editorial office.”
Milestone: “38 minutes into this match, team B will be the first team ever in
the series to play a 1000 minutes with no penalty against them.”
Human interest: “Left midfielder B plays in honour of his terminally ill sibling.”
2 What is a News Angle?
Certain events are so remarkable that they are newsworthy in themselves. Other
events need to be presented in a certain way to become interesting for its read-
ers (listeners, viewers). Several decades ago already, Altheide [2] observed that
reporters rely on “‘angles,’ or story lines, which give the specific events new
meaning”, to which Shoemaker and Reese [30, p. 115] add that “[a] predefined
story ‘angle,’ [. .. ] provides reporters a theme around which to build a story”.
They also mention “news values [that] distil what people find interesting and
important to know about” [30, p. 106].
2.1 Definition
We define a news angle tentatively as how a journalist or other news profes-
sional makes an event interesting for an audience. As an example, Table 1 lists
alternative angles on the same event: a football game (we will go on to analyse
the impact angle in more detail below). In addition to gaining the audience’s
attention, a news angle such as these serves several additional purposes:
–it provides a criterion for selecting events that are worth reporting;
–it points towards additional facts to report;
–it suggests which information sources to use; and
–it can serve as a template for how to present the event.
We focus more on the first three than on the fourth. Using basic concepts from
literary theory [10], we focus more on what is told (the fabula) than how it is
told (the discourse), which together form a narrative. Hence, finding an angle
on an event is a creative but fact-based task. It takes as input a limited factual
description of an event and produces as output a richer description that contains
additional facts that are related to and augment the event and that connects the
core facts to the interests of the audience.
4 M. Gallofr´e Oca˜na et al.
Of course, there is no such thing as neutral factual content. Journalists and
editors continuously choose which events to report, how visible to make them,
who to interview, which other data sources to use, and how to word the final
story — a phenomenon often referred to as news framing or slanting. Even seem-
ingly objective big data collected by surveillance cameras or other sensors are,
in the end, products of human choices of whether and where to place the cam-
eras and sensors and of how to analyse and disseminate the captured data [16].
Yet computer-assisted journalism may in the future serve to limit — or offer
alternatives too — human framing and slanting of the news.
2.2 Example: The impact angle
Several researchers, such as [30], have listed common news angles used by journ-
alists. Additional lists have been provided by practitioners [33,29]. In future
work, we want to synthesise these and other reviews into a taxonomy of news
angles. As an example, this paper will discuss one of them, impact, in a little
more detail according to: how it is described in the literature, its most common
subtypes, its indicators, the data sources available to assess the indicators, and
whether and how the angle amplifies and/or is amplified by other angles. The
purpose is to better understand the requirements for a big-data platform that
can support this and other angles.
Description The literature describes the impact angle in various ways (of which
some are perhaps angle subtypes):
–Prominence: “The importance of a story is measured in its impact: how many
lives it affects. Fatalities are more important than property damage.” [30]
–Disaster: “Describes the impact of negative situations (and usually either
what brought them about, how it’s affecting the new subject, or what’s
being done about it).” [33]
–An incident: “Anything that goes wrong has the potential to become news-
worthy, such as an industrial explosion, car crash or school shooting.” [29]
Types of impact Events can be impactful in several ways, including: loss of life,
physical injury, mental distress, damage to the environment, loss of property,
and damage to property, including public infrastructure. Impact can thus be
subdivided accordingly into: human impact, environmental impact, damage to
property, etc. We envisage a big-data architecture where specialised agents for
each subtype (or subsubtype etc.) continuously crawl a knowledge (RDF) graph
in search of impactful events that can trigger a variant of the impact angle. Other
agents can search for indicators of other angles, such as groups of reports that
describe the same event with very different sentiments (potentially a subtype of
conflict) or events that are related to an influential person.
Towards a Big Data Platform for News Angles 5
Indicators and data sources Indicators of human impact are: loss of life, physical
injuries, and mental distress, which can be gleaned from analysis of small as
well as big data sets. Loss of life and physical injuries can be lifted in real-time
from the official social-media feeds and online hospital logs if they are available.
Otherwise, they must be synthesised from other news reports or, using triangu-
lation, from less trusted social-media sources. Mental distress in an area can also
be identified through large-scale sentiment analysis of social-media messages.
Damaged infrastructure can be indicated by and triangulated from a range
of sources, such as surveillance cameras and other sensors, citizen reports on
social media, messages from public authorities, deviating arrival times of and
timetable changes for public transport. Environmental impact and damage to
property can be derived from many of the same sources.
Estimating past impacts from archival materials can be much easier, as au-
thorities and open data sources maintain statistics, for example, of accidents and
disasters by type and various measures of impact.
For impact types such as these to be identifiable by the agents that operate
on the knowledge graph, the represented events must be continuously enriched
with additional types of information both from small-data sources like public
authorities, trusted news sources, and official social media accounts and from
big-data sources like social media and the Internet of Things (IoT).
Interactions High-impact events are newsworthy in themselves, and the core
facts established by the agents can be presented to the audience more or less as
is. Lower-impact events can also turn out to be interesting: either because there
are (potential) secondary consequences, such as a limited avalanche blocking
a train line during the holiday season, or because they are amplified through
interaction with other angles, such as proximity or influential people: a minor
flood in a residential area can become global news if it fills the basement of a
celebrity’s home.
2.3 Audience and genre expectations
A news angle is (almost always) relative to an audience: in case of the influence
angle, different audiences may have widely different views of which people are
famous and, to a lesser extent, powerful. News angles rely on the type of events
that interest the intended audience [23]. For example, the angles and topics that
interest people who read a local newspaper context are quite different from those
of the international news section of BBC World. Indeed, analyses of media users
in order to better understand their preferences and habits is itself a big-data
analysis problem.
News angles are also influenced by the general characteristics of the news
market. Traditional journalism is undergoing an economic crisis due to online
news competition, and many newsrooms have had to trim their staff while produ-
cing more news than ever [31]. This leads to variations of copy-paste journalism
and click baits. Adjustments have also been made to adapt to the online news
6 M. Gallofr´e Oca˜na et al.
market [5,11]. Higher-level journalistic tools that support news angles is a prom-
ising way of improving both quality and productiveness in a time of crisis and
hard competition.
For each angle type and indicator, newsworthiness criteria can be established,
taking into account: the market addressed by the newspaper, the characteristics
of the audience, and the genre of the given news story. Optimising newsworthi-
ness criteria for different media forms and genres is an empirical problem that
can potentially be answered with big-data analytics, comparing factual descrip-
tions of past events with news criteria most prevalent in the audience of the
corresponding news reports. Multiple angles on the same event can be possible.
Sometimes, only the best one should be chosen; other times, two or more of
them could be combined to suggest a better story or to reach different niches of
readers (listeners, viewers).
3 Towards a Big-Data Architecture for News Angles
News Hunter is an evolving architecture and proof-of-concept prototype for sup-
porting journalistic work, which has been developed by our research group in
collaboration with Wolftech AB, a supplier of newsroom software systems for
the international market [27,3]. It has been designed to continually harvest news
items and social media messages from the web; analyse and represent them se-
mantically in a knowledge graph; classify, cluster, and label them; enrich them
with additional information from encyclopedic and other reference sources; and
present them in real time to journalists — either as tips about new events or as
background material for stories they are already working on.
3.1 Current News Hunter architecture
The current version of News Hunter comprises the following components:
–Harvesters continuously download news texts and other relevant data items,
such as social-media messages, from the web.
–Uploaders load harvested data into the appropriate database.
–The TextDB stores textual data items such as news stories and social media
messages in raw form.
–A (currently online) Translator translates other-language texts into the ca-
nonical language, which is currently English.
–The GraphDB represent harvested data items semantically as knowledge
graphs in RDF format. (In the GraphDB, each data item is also known as
an event, although they may not be important enough to be called news
events.)
–The Lifter represents each text in the TextDB as a knowledge graph in
the GraphDB with the coordinated aid of several more specific analysers:
concept extractors identify the central keywords in the text and disambiguate
their meaning. Topic analysers identify the central topics the text is about,
Towards a Big Data Platform for News Angles 7
Figure 1. The front-end of the current News Hunter prototype. Relevant (named)
entities and concepts that are extracted from the editor panel on the left-side are listed
in the right-side panel, in which clicking an entity or concept returns a list of stories
and other texts that are related to the one being typed into the editor.
independent of the keywords that are used. Named-entity analysers identify
the own names of individuals, such as the people, organisations, and places
that are mentioned. Sentiment analysers identify the positive and negative
emotions in the text and its various phrases. Categorisers (or Labellers)
assess how well the text fits predefined taxonomies, such as the IPTC News
Codes [14].
–An Event detector identifies bursts and other changes in the occurrence fre-
quencies of concepts, topics, named entities, and sentiments in a geographical
or social region.
–A (currently limited) Enricher extends the core knowledge graphs produced
by the Lifter with additional semantic reference data retrieved from the LOD
cloud and from proprietary sources.
–ASocial networker performs basic social-network analyses on the graph (cur-
rently limited to focussing on affinities).
–The Editor lets journalists write up new stories, which the Lifter continu-
ously analyses semantically.
–A (currently limited) Retriever uses the semantic analyses of the new story
to identify relevant background information in the GraphDB and retrieve
related stories and other texts from the TextDB.
–The Front end (Figure 1) contains the editor and presents relevant back-
ground information and related stories and other texts to journalists and
other news professionals.
3.2 Current News Hunter technologies
The current prototype [26,4] is mainly written in Python and C# as an ASP.NET
application. Its components are interconnected through REST APIs [7] in a
Flask-based micro-service architecture.
8 M. Gallofr´e Oca˜na et al.
Figure 2. Extending News Hunter to support news angles.
The Harvester component uses a Python script to collect news-related texts
(also called data items) from a variety of sources, such as Facebook, RSS, and
online newspapers. Current focus is on downloading and parsing RSS feeds into
JSON files using Feedparser. The JSON files are then stored in raw form in an
Elasticsearch TextDB and, if necessary, in English using Microsoft’s Translate
API. The (English-language) JSON files are sent to a C# .NET pipeline that
analyses the texts semantically in terms of: concepts, topics, named entities,
sentiments, and categories. The lifted data is then stored in a BrightstarDB
GraphDB (or triple store), which can be queried using SPARQL though a Mi-
crosoft LINQ .NET component. The news-related texts (data items) represented
in the knowledge graph are also clustered in order to detect new events.
The different analysers use a variety of tools and techniques such as Tex-
tRank,TF-IDF,SVM (support vector machines), MLP (multi-layer perceptron),
RAKE (Rapid Automatic Keyword Extraction), and DBSCAN clustering, among
others. Both SVM and MLP were implemented using the Keras Python library.
DBSCAN clustering was implemented using the Scikit-learn Python library. TF-
IDFs were calculated with the Textacy and Spacy Python libraries. Sentiment
analysis was done using the AFINN Python library.
The front-end application was written in HTML and CSS combined with
AngularJS, and it was prototyped using Sketch and Marvel.Froala was used as
text editor. An overview of News Hunter is presented in [3]. For further details
on the tools, techniques, configurations, and evaluations we have used, see [26,4].
Towards a Big Data Platform for News Angles 9
3.3 Leveraging big data for news angles
The current prototype does not yet scale to big data and does not support news
angles. We are therefore evolving News Hunter into a big-data architecture that
can be extended with components that support news angles.
The new architecture must be open-ended in two ways: It must allow user
organisations to interface with their existing back-end tools, such as existing
semantic and other analysis services and existing text and graph databases. It
must also allow them to use open and proprietary data sources in combination.
For open data, News Hunter may provide storage and analysis as a service. But
news organisations may also want to use News Hunter to store and analyse pro-
prietary data, either self-produced or licensed. The architecture should therefore
be able to combine cloud and local data storage and analysis as seamlessly as
possible.
The (orange/yellow) graph on top of Figure 2 illustrates some of the high-
level reasoning steps needed to support news angles, whereas the (blue/green)
grid at the bottom shows the different types of information that must be dealt
with. Along the vertical axis in Figure 2, information can be either: Journalistic,
meaning that it is text written by professional journalists. Textual, meaning that
it is textual, but not written by professional journalists. Other, meaning that is
is non-textual, which currently means that it is represented as knowledge graphs
represented in RDF. Of course, future versions of our architecture may also cover
other information types than texts and knowledge graphs, such as other types
of structured data, along with images, audio, and video, introducing additional
rows in Figure 2 and requiring additional analysis and storage techniques such as
speech-to-text conversion, image/video analysis, and other types of databases.
Along the horizontal time axis, the information can be either: Archival, rep-
resenting past events. Breaking, representing currently unfolding events. Work-
ing, representing not-yet-reported events. Future, representing anticipated, pre-
dicted, scheduled, or recurring events.
Each event is represented as a small event graph, represented as RDF, with
both a core that describes the event directly and an extension that provides
context. The central resource (or node) in the core graph represents the event
itself, with related resources that result from lifting event data to semantic form.
For example, journalistic stories and other texts can be lifted using techniques
such as concept extraction, topic identification, named-entity recognition (of
people, places, organisations, works, etc.), and sentiment analysis. The exten-
sion graph results from enriching the core graph with additional information
in RDF format, for example from open semantic reference data sets in the
Linked Open Data (LOD) cloud — such as DBpedia, Wikidata, GeoNames,
and LinkedGeoData — or from proprietary data sources that have been lifted
to semantic format.
Event graphs will usually overlap to a large extent because they include the
same RDF resources for: people, places, and organisations; concepts, topics, and
categories; RDF types; etc. Figure 2 therefore indicates that multiple overlapping
(or otherwise similar) smaller graphs will be clustered and merged to form larger,
10 M. Gallofr´e Oca˜na et al.
Figure 3. Overview of the new architecture for News Hunter.
more detailed, and reliable graphs. Exploiting overlapping and similar event
graphs in this way is essential both for generating richer (more complete and
detailed) event descriptions and to corroborate them. In particular for social
media messages, unless the originator is known and trusted, triangulation of
information from several independent sources is essential to ensure that only
reliable event data are reported as news.
3.4 A lambda architecture that supports news angles
We are currently evolving News Hunter into a big-data architecture that can be
extended with components that support news angles along the lines shown in
Figure 3, on top of Apache’s Kafka platform1.
The new architecture is based on the Lambda architecture pattern [22], which
is designed for service-oriented big-data processing and is able to analyse big data
from social media sources with satisfactory performance [28]. An advantage of
the Lambda architecture — as opposed to the alternative Kappa architecture [17]
— is that it supports both real-time streaming analysis of all incoming data items
and batch-oriented deeper analyses (and re-analyses) of selected data items that
later turn out to be particularly interesting.
Harvesting system The new architecture is designed for continuously gathering
news-related information from a variety of sources through the harvesting sys-
tem, which is conceived as a message publishing and subscribing system. This
pub-sub system lets News Hunter connect to a wide variety of external data
sources, from social networks via commercial news services to the Internet of
Things (IoT). It will filter and prioritise the incoming data streams and store
the raw data in a data lake. Built on top of Apache Kafka1, it provides a scal-
able and parallel messaging mechanism. The harvesting system will comprise the
Harvester components in the current architecture and add a new Filter and/or
Prioritiser component.
Data lake The data lake stores incoming data items in their raw form as Kafka
topics, along with their English translations. It will comprise the TextDB and
Translator components in the current architecture, and we will consider adding
more powerful big-data storage technologies as the tool evolves.
1https://kafka.apache.org/
Towards a Big Data Platform for News Angles 11
Knowledge graph (Semantic news) The knowledge graph contains semantic triples
that are lifted from the data lake in real time as new data items arrive. Data lift-
ing consists of concept extraction, topic identification, named-entity recognition,
sentiment analysis, categorisation/labelling, and relation extraction. The graph
will comprise the GraphDB and Lifter components in the current architecture,
and we plan introducing a Relation extractor component.
News analysis The analysis layer analyses the lifted data items further, in
real time and possibly as batch. Streaming news analysis in real time takes
semantically-lifted data straight from lifting and is intended to provide journ-
alists with both (1) real-time updates of the stories they are working on and
(2) potentially newsworthy new events. Semantic news analysis in batch takes
semantically-lifted data stored in the knowledge graph and is intended to (3) provide
journalists with background information related to the stories they are working
on and (4) organising and enriching the knowledge graph with data from other
sources; performing social network analysis on the graph to identify super-nodes,
sub-networks, affinities; and detecting clusters of overlapping or similar events.
The streaming news analysis will comprise the Event detector,Enricher, and
Social networker components in the current architecture. In order to support
news angles and other higher-level services to journalists, the analysis layer will
also explore new components such as Organisers that continuously assess and
improve the structure to the knowledge graph. Analogy reasoners that aim to
identify other less obvious but semantically deeper connections between past and
present events and stories and related background information. Anglers leverage
the semantic analyses and background information to identify, generate, and rank
candidate angles on potential news events that a journalist is already working
on or that have been detected in the knowledge graph.
Service layer The service layer is in charge of making all the knowledge and
analysis results available to newsrooms through a GUI and a REST API. It is this
layer that makes the architecture and tool available to journalists and other news
professionals. It will offer dashboards and user interfaces that present potentially
relevant angles, stories, and other background information to journalists based
on their current activities and preferences. The service will comprise the Editor
and Front end components, and it will extend the Retriever.
4 Conclusion
The paper has discussed how journalistic tools can be improved by combining
open and big data sources with the concept of news angles. This is an import-
ant research problem, because news work is becoming increasingly computer-
assisted and big-data based, creating opportunities for a new generation of tools
that provide even higher-level support for journalistic work. We have clarified
and discussed the central characteristics of news angles and related terms and
outlined how our architecture and tool for journalistic work, News Hunter, can
be evolved and extended into a big-data platform that supports news angles.
12 M. Gallofr´e Oca˜na et al.
Our work is part of a research project, News Angler, that is carried out
in collaboration with Wolftech AB, a supplier of newsroom software systems
for the international market. The News Angler project has two primary goals:
(1) to improve and evolve the News Hunter architecture towards a big-data
architecture that scales to the needs of international news organisations and
(2) to extend News Hunter to support news angles. The project thus combines
a moderate-ambition research and development goal (1) with a higher-ambition
basic research goal (2). While eyeing the second, longer-term goal, the present
paper also lays out concrete steps towards the first.
Our work on news angles is only beginning, and a long line of research and
development issues remain. On the architecture level (1), are porting the News
Hunter prototype to Linux on top of Apache’s big-data stack, leveraging tools
such as Kafka, Cassandra, and Spark. Most of the current components will have
to be reengineering or reimplemented as part of this effort. Many of them should
also be extended and improved in the process (in particular the Enricher,Social
networker, and Retriever), and some new components introduced (for example
the Filter,Relation extractor,Organiser,Analogy reasoner, and a locally-running
Translator). In order to make our platform open-ended, we need to define clear-
cut APIs between our components, (a) to make it easier for user organisations
to interface with their existing back-end tools, (b) to make it easier for user
organisations to use open and proprietary data sources in combination, and
(c) to make it easier for ourselves to combine and compare alternative component
implementations, such as multiple named-entity recognisers.
On the news-angle level (2), we want to develop new components that con-
tinuously analyse the input stream and knowledge graph for new events and
newsworthy angles. One component type will manage prerequisites for angles,
for example agents that identify approaching anniversaries; conflicting descrip-
tions of an event; natural disasters and their impact; swift changes in popularity
or political power; etc. Another component type will match angles to (breaking
or historical) events, possibly combining multiple angles on the same event.
We think our work on the News Hunter prototype and its News Angler com-
ponents can contribute to the wider research literature in several ways. Relation
extraction is an research area that is central for our project. Currently, our event
graphs tend to have a star-like structure, with a central RDF node representing
the event itself and with lots of related annotation nodes that are connected to
it, but less often to one another. Yet it is the relations between concepts, topics,
named entities, and sentiments that describe an event most precisely. Gangemi
et al. [8] have recently proposed FRED, a library and online tool for relation
extraction that may be useful for our purposes. The emerging generation of
neural-network base compositional vector representations of word meanings [24]
may also offer new ways to extract relations from text. Relating events by ana-
logy is another interesting research task, along with, e.g.: collecting and creating
taxonomies of news angles; developing a user-friendly way of reading and writ-
ing news angles; identifying interesting and unexpected news angles; and fully
exploiting open data, in particular linked open data.
Towards a Big Data Platform for News Angles 13
Acknowledgement
Early development of News Hunter was supported by NCE (Norwegian Centre of
Expertise) Media. News Angler is funded by the Norwegian Research Council’s
IKTPLUSS programme as project 275872. The authors are indebted to Arne
Berven and Bjarte Djuvik Næss at Wolftech AB for fruitful discussions and to
Kamal Alipour, Ole Andreas Christensen, Kjetil Jacobsen Villanger, and Sindre
Moldeklev who made central contributions to the earlier versions of News Hunter.
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