WORKSHOP REPORT

Report on the 1st International Workshop on
Recent Trends in News Information Retrieval(NewsIR16)
Miguel Martinez1
Frank Hopfgartner4

Udo Kruschwitz2
Gabriella Kazai3
David Corney1
Ricardo Campos5
Dyaa Albakour1
1
Signal Media, UK{firstname.lastname}@signalmedia.co2
University of Essex, UK
udo@essex.ac.uk
3Lumi, UKgabs@lumi.do4
University of Glasgow, UK
frank.hopfgartner@glasgow.ac.uk
5
Polytechnic Institute of Tomar / LIAAD-INESC TEC, Portugal
ricardo.campos@ipt.pt

Abstract
The news industry has gone through seismic shifts in the past decade with digital content
and social media completely redefining how people consume news. Readers check for accurate
fresh news from multiple sources throughout the day using dedicated apps or social media
on their smartphones and tablets. At the same time, news publishers rely more and more onsocial networks and citizen journalism as a frontline to breaking news. In this new era of fastflowing instant news delivery and consumption, publishers and aggregators have to overcome
a great number of challenges. These include the verification or assessment of a source’s
reliability; the integration of news with other sources of information; real-time processing ofboth news content and social streams in multiple languages, in different formats and in highvolumes; deduplication; entity detection and disambiguation; automatic summarization; andnews recommendation. Although Information Retrieval (IR) applied to news has been a
popular research area for decades, fresh approaches are needed due to the changing type andvolume of media content available and the way people consume this content. Hence, thefirst international workshop on recent trends in News Information Retrieval (NewsIR) was
held in conjunction with ECIR 2016. As part of the workshop, we released a new dataset
consisting of one million news articles to the research community. The workshop was very

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well attended with around 70 registered participants. We received a healthy number of 19
submissions in total of which 12 were accepted for presentation. In addition to that, we werepleased to have two keynote talks by well-known experts in the field - on with an industry
background (Jochen Leidner) and one from academia (Julio Gonzalo). The workshop also
included a breakout session to discuss ideas for a future data challenge in news IR and closedwith a focused panel discussion to reflect on the day. Throughout the day the workshop
stimulated discussions around new and powerful uses of IR applied to news sources and theintersection of multiple IR tasks to solve real user problems. In particular, several ideas were
presented on solving complex information needs for media monitoring, event detection andsummarisation. Moreover, and going forward, the workshop concluded with a long list ofsuggestions for shared tasks, and dataset requirements.

1

Introduction

News from mainstream media outlets is often one of the most relevant, and influential sourcesof information. This ranges from the influence that newspapers may have on elections to
the reputational damage that a negative article in a well-known magazine can cause to abrand. The process of consuming news itself is constantly changing. We receive a continuous
influx of news information from different sources (e.g., traditional newspapers, blogs andsocial media) and this has had a massive impact on the nature of information systems. Someof the current challenges we are facing are the integration of news data with other sourcesof information such as social media [1]; real-time analytics [2]; processing text in multiple
languages; automatic temporal summarization [3]; and scalable processing of millions ofarticles on a daily basis.
Following discussions at ECIR 2015 we created a forum1 to discover if there was enoughinterest within the IR community for a workshop focusing on online media, and news data inparticular. Around 40 members joined the forum straightaway and several fruitful discussions
started. This was a clear indication for the strong interest in the community for organizing
such a workshop. Furthermore, the discussion in the forum illustrated the diversity of topics
that this workshop aimed to explore, which include among others: traditional and social
media integration, event and anomaly detection, credibility, bias and polarity, temporalsummarisation, and data visualization.
Overall, the workshop aimed to bring together scientists conducting relevant researchin the field of news and information retrieval to facilitate discussion and debate about the
problems they face and the solutions they are exploring. Moroever, to stimulate workshop
participation (and more generally to provide a useful resource for researchers in the area), we
have released a new dataset of one million recent news articles from a wide range of sources:
The Signal Media One-Million News Articles Dataset (Signal-1M)2 [4]. In contrast to many
existing collections (such as Reuters-21578 and Reuters RCV1), the dataset includes news
articles from a wide range of sources including global, national and local newspapers, alongwith magazines and blogs. This dataset is released under the standard Creative Commonslicence3 to encourage re-use in diverse non-commercial research projects. Furthermore, inthe call for papers, we encouraged submissions of experimental results on our new dataset.
1

https://groups.google.com/forum/#!forum/news-ir
http://research.signalmedia.co/newsir16/signal-dataset.html
3
https://creativecommons.org/
2

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We believe that one or more shared tasks or challenges will emerge and that, with suitablerefinement, these may form the basis of future workshops.

2

Workshop Programme

Each submitted paper was reviewed by at least three members of the programme committee.
This was followed by a discussion period among the workshop organisers as a result of
which decisions about acceptance/rejection were made. The Workshop programme included
two keynote talks representing both academia and industry, full paper presentations and aposter session, finishing with three breakout groups followed by a panel. The full workshop
proceedings were published in the CEUR Workshop Proceedings4 [5].
The one-day workshop was very well attended with around 70 registered attendees andwas structured as four sessions: two in the morning and two in the afternoon. In the firstsession, the workshop was kicked off with an introduction from Miguel Martinez providing
a motivation and a summary of the overall objectives for holding the workshop. The introduction also included a summary of participation figures, which reflect the success of theworkshop where a total of 19 submissions were received resulting in 12 accepted papers.
Following this, the first keynote talk was presented by an industry representative, Jochen
Leidner from Thomson Reuters. After the keynote, authors of full papers presented shorttalks. These talks were short as the authors had the opportunity to discuss their work indetail with attendees during a poster session in which authors of all papers (long and short)
were able to present posters and/or demos. Indeed, the poster session was a success as canbe inferred from the crowded corridors in the conference venue and the informal feedback
we got from participants. The poster session marked the end of the second session and thefirst half of the day. The third session started an academic keynote by Julio Gonzalo from
UNED. After that, Dyaa Albakour (Signal Media) presented the Signal-1M dataset which
was released as part of the workshop to foster participation from the community and toprovide a basis for a data challenge track in news IR. This paved the way for the breakout
group discussion where each group was asked to discuss challenges and requirements for amodern data challenge track in news IR. A representative from each group presented theirfindings to the wider audience. Finally, the workshop was closed with a panel discussion thatreflected on the day and the various research challenges pointed out during the workshop.
In the following, we detail the workshop’s activities outlined above. To give the reader aflavour of the workshop’s presentations and discussions, in Figure 1, we show a word cloud
generated from the tweets posted on the workshop day mentioning the account @newsir16.
We discuss these activities under the various themes that were covered by the talks and the
keynotes. In particular, to structure the discussion, we identify three main themes whichhave no clear boundaries and definitely overlap. These themes are Media Monitoring &Insights, News Events and Analysis & Visualisation.

2.1

Theme 1: Media Monitoring and Insights

This theme is a broad one and it refers to a wide range of IR tasks and applications where
the aim is to provide professional users with timely access to information of their interest.
This ranges from real-time filtering of news articles for a certain brand or certain topics, to4

http://ceur-ws.org/Vol-1568/

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Figure 1: A word cloud generated from the contents of the @newsir16 tweets

more complex tasks such as providing market intelligence tools by mining large amounts ofnews data from main media outlets, blogs and social media.
The keynote by Jochen Leidner [6] presented views of research in this area and the challenges that the IR community faces in such contexts. These views are supported by thevarious real-world use cases presented in the keynote. He started with the early days innovations in Thomson Reuters where the founders used pigeons to “be quicker than the news”
that were being distributed by ship. Going several decades ahead, Thomson Reuters is now
investing largely in the domain of media monitoring and insights. An interesting exampleof this is detecting the side effects of “popular” medicines by exploiting social media (i.e.
Twitter). One of the challenges for doing this is the sparsity of the tweets. For a total of
500M tweets that are generated in a given day, 721 of them mention one of the 2,200 morecommonly bought drugs. Given these tweets, it is still possible to find correlations and unfold patterns of unknown side effects that should be documented. Also, in some cases, suchside effects could potentially be used to cure or address other illnesses. Another example
is building a risk profile for a company from public media. This requires a combinationof heuristics, machine learning and human intervention to understand situations that will
potentially jeopardise some companies. The take-away message from this talk is that the
future of media monitoring is transitioning from “delivering news to delivering actionable
intelligence”, which surfaced the aforementioned tasks.
The second keynote in the afternoon session by Julio Gonzalo [7] also touched on thespecific topic of reputation management in social media and the lessons learnt in tackling
reputation management within the RepLab Evaluation framework. A typical task for reputation management is sentiment analysis of news articles and social media posts. Julio
highlighted the challenges of performing sentiment analysis in this context. This includes
differentiating between opinions and polar facts. For instance “Tesco is firing 15,000 people because their shares are dropping” would be a very negative story about Tesco, but
an objective one nevertheless (assuming it is a true story). In addition, sentiment analysis
for reputation management should take perspective into account. For example, “Antena 3buys la sexta” is positive news for investors but may be negative for citizens. Finally, JulioACM SIGIR Forum

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reminded us that sentiment polarity is not always a measure of reputation.
Media monitoring applications have made their way into some of the workshop papers.
For example, Igor Brigadir presented a paper on detecting attention dominating moments
in online media [8], i.e. moments in which people talk about the same thing with regards
to a specific topic. The paper identifies these moments by looking at points of time where
the diversity of documents drops significantly. The paper shows examples of applying thisapproach to news articles and blogs in the Signal-1M data set [4], along with a parallel Twitter stream in September 2015. The examples show how this approach can detect attention
moments that are related to real-world events documented in the Wikipedia events portal.
While this paper considers diversity to detect attention, the paper by Prati et al. [9] looked
at a very related concept to diversity which is novelty. In particular, the authors applied arecent model for novelty dynamics to analyse news coverage. In their analysis, their main
variable to define the concept of novelty is the lead newsmaker. Their analysis uncovers
interesting insights for different media sectors and topics. For example, their analysis showsthat newspapers have the largest novelty in introducing new lead newsmakers, while crimeis the topic with the most novelty.
Finally, Suzan Verberne presented a user study [10] on traditional media monitoring,where professional users use very long and complex Boolean queries with keywords to specify
their information needs. In particular, the user study investigated the usefulness of querysuggestions in this context to aid the professional users in refining their queries. High precision is not an issue for these professional users and they are generally satisfied with results
without any relevance ranking. However, for generating relevant query suggestions, the relevance ranking becomes important as the quality of the query suggestions is dependent on
a good ranking of documents and users would abandon non-relevant query suggestions.

2.2

Theme 2: News Events

Detecting, summarising and filtering events in news has been a popular research topic in IR
and it has definitely dominated some of the talks presented and the dicussions in the workshop. To this end, the paper presented by Andrey Kutuzov and Elizaveta Kuzmenko [11]proposes yet another application of neural word embeddings [12] to detect real-world eventsin the news. Their approach involves learning word embeddings on a large reference corpus
and then successively updating the model from daily news using the Signal-1M dataset [4], as
well as a Russian news collection. For a given entity, they can estimate the association shift(semantic shift) over time where any significant shift may be triggered by an occurrence of anevent. Their analysis on country names shows the promise of the approach, where they wereable, for example, to automatically detect a shift of associations for Chile to earthquakes
on the day when an earthquake took place there. Similarly, the poster presented by Basile
et al. [13] proposes an alternative approach for finding temporal shifts in word meanings as
results of real-world events. In particular, the authors apply Temporal Random Indexing
to build several geometrical spaces of word meanings called Distributional Semantic Models.
They also used the Signal-1M dataset [4] to validate their approach. Their approach wasable to detect the shift in the contextual use of the word ‘scandal’ during the third week of
September 2015. In particular, and as a result of breaking news on the Volkswagen emission
scandal, words like ‘volkswagen’, ‘automaker’ and ‘diesel’ became direct neighbours of theword ‘scandal’.
While the previous papers focused on detecting events, Michael Bender from Thomson

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Reuters presented work [14] on clustering news articles around the sub-topics of an event.
The seed clusters for an event are identified from editorially supplied topic labels called“sluglines”. Their approach involves fuzzy deduplication of articles to identify highly similar documents and then clustering them using similarity based on text, entities and topics.
Since ground truth data of ideal clusters is hard to obtain, the authors opted for a qualitativeevaluation of this approach with human assessments on the cohesiveness and the accuracyof the clusters using a 5-point Likert scale.
Finally, Gregor Leban presented a paper [15] on the Event Registry system5 . The motivation behind developing this system is trying to move away from traditional news consumption
platforms where users are overwhelmed with duplicates of popular and dominating stories.
This would also prevent them from discovering the “long tail” which may be of interest.
Their approach is based on semantic annotations on the document level, e.g. entity linking,clustering news articles, cross-lingual clustering and event construction (driven by the clustering step). Using these components, they are able to show events rather than articles toprovide a more general understanding.

2.3

Theme 3: Analysis and Visualisation

There were several papers and posters that exploited, analysed or visualised news for a variety of tasks. An excellent example on how news can be exploited for other IR tasks waspresented by Stefano Mizzaro [16]. Stefano and his co-authors propose to categorise tweets
with collection enrichment using new sets of words, extracted from news on webpages of thesame temporal context. They tested three different features of news namely volume, variety
and freshness. The experiments confirmed the importance of all of these features. The paper
was not just well received but also won the Best Paper Award. Svitlana Vakulenko presented
a demo [17] where tweets are used in the opposite direction. In particular, the authors proposed to build a bipartite graph with two types of nodes, namely tweets and news, and two
types of edges: (i) explicit edges from tweets to news articles they cite, and (ii) implicitedges infered from a similarity between news articles using a bag-of-relations representation
constructed with NLP techniques (since news sources do not cite each other). In practicethese implicit edges serve as network diffusion models to uncover the latent relations between
news articles which can be useful to journalists for example to understand the flow of a story.
As discussed in Section 1, the Signal-1M dataset was released to foster research in news IR.
Indeed, several papers in the workshop used the dataset to validate and evaluate the proposed
approaches (c.f. Section 2.1 and Section 2.2). Dyaa Albakour from Signal Media presented
the dataset [4] outlining the motivations for releasing it in the first place. Traditional news
datasets are often drawn from one source and have become outdated. This dataset aims toaddress this gap by covering a large number of sources representing main media outlets, lesspopular media sources and even blogs. Dyaa also presented some insights from an initial
analysis of the dataset. For example, by computing similarity between all possible pairs ofarticles, we can observe a substantial ratio of duplicates (on average 2.2 duplicates per article). Moreover, an analysis of the daily and hourly distribution of articles uncovers publishingpatterns across different media types and in different regions (e.g. peaks during early morningof certain timezones). This was echoed in the presentation of Sergio Nunes [18] who demonstrated the MediaViz Project6 , which provides a visualisation tool to explore the dataset. In
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http://eventregistry.org
http://irlab.fe.up.pt/p/mediaviz/newsir/

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particular, the tool plots graphs of volume and allows the user to explore the diversity overtime as estimated by a combined distance metric between all n-gram daily vectors.

2.4

Breakout Groups

For the breakout groups, to initiate the discussion, we raised hypothetical questions to theparticipants. The questions were:• If we organise this workshop next year, and we want to introduce a shared task (challenge) to work on, what will the task(s) be?
• What datasets do we need to evaluate it?
Answering these question will help us in understanding the priorities of the community
around news IR in addition to building datasets and evaluation frameworks to foster research in this area. Participants formed three groups and each group discussed the questions
and reported their findings to the workshop. We summarise their findings in the following:• Although the Signal-1M dataset [4] was well received by the participants the groups
pointed out some of its limitations and suggested some additions. Firstly, the onemonth range chosen for it (September 2015) makes it difficult to be used for tasks that
require mining temporal patterns over longer time spans, such as monthly or quarterly
patterns and therefore it was suggested that a longer time span is required. Othersuggestions include the integration with other information sources like multimedia content from articles or more multilingual documents. Another very common request wasaligning a Twitter dataset over the same period to have a unified collection. This will
be very valuable for a multitude of different tasks, such as reputation management. The
Twitter collection presented in the workshop by Brigadir et al. [8] is a good candidate.
Finally, one group suggested to align the dataset with popular knowledge bases andpublic ontologies.
• For the shared task, a lot of different ideas were brought forward by the groups. Someof the most common were temporal summarisation, entity linking and disambiguation,
diversity and sentiment analysis. In general, there was a tendency that the task should
be a complex one, as opposed to adhoc retrieval, and should replicate the jobs of professional users such as journalists. However, one group suggested that in addition to
a complex task, and in order to encourage more participation, a future data challenge
track should also include a simple one that involves labeling some data. Other emerging problems that were mentioned several times in the breakout discussions include
media bias detection, the evolution and the verification of news. The evaluation of thepotential tasks surfaced the discussions and were brought forward to the panel.

2.5

Panel

For the panel, we invited our two keynote speakers, Jochen Leidner and Julio Gonzalo to
be joined by Gabriella Kazai and Stefano Mizzaro. This panel represents both the industry
world, as well as the academic one, with expertise in a multitude of fields in IR and NLP being represented by the experience and knowledge of the panelists. The questions raised to thepanelists stemmed from the various talks and the discussions during the day. In particular,
the panelists discussed the following topics:

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• Evaluation: Are me measuring the wrong thing? Using the right evaluation
metric is a common challenge in IR tasks. Moving towards more complex tasks (e.g.
reputation management) implies that we should focus on user satisfaction when designing the evaluation process. Stefano and Julio also mentioned that IR has a multitudeof evaluation metrics already. We probably do not need new ones, but we need to use a
multiple of them to provide better explanations. Jochen suggested to involve and invite
journalists to the next NewsIR event to help us better understand the user model whencoming up with an evaluation metric.
• Social media, News and Blogs. Are they different at all? The media interaction
landscape has dramatically changed in the last decade or so. Traditional news outlets,
blogs, social media and personal journalism are now intrinsically linked and the boundaries between them are becoming thinner as time passes. Therefore, it is challenging
to classify some of the internet sources as news or blogs. The panelists agreed that the
space is getting more complex with clear dependencies between some of the different
media. For example, it is not uncommon that a tweet causes someone to create a short
blogpost, that in return causes someone to verify a story and then write a short piece ina local newspaper that will eventually be picked up by a major worldwide publication.
This symbiosis should be acknowledged and addressed by the community. On the otherside, while some blogs could be seen as an authority in a topic, a large portion of thesocial media space could be considered of low journalistic quality, with low (or none)
verification of news and a high likelihood of spreading rumors. This highlights theimportance of automatically distinguishing between pieces of real news, pseudo-news,
rumours and clickbait.
• How important is Trust and how can we help measuring it? Trust is a critical factor in news that has been the foundation of real journalism since its inception.
However, the explosion of new sources of information has increased the difficulty ofknowing what sources are legitimate or trustworthy without doing detailed research. A
very important question then is what do we mean by ‘trustworthy’. We live in a world
where given a world event, we can find several points of views, that may contradict
each other. What source do we then label as ‘trustworthy’ ? We can indeed compute
who are the main influencers in a given space, but we face a more difficult challenge
when we try to measure if the same person is also an expert in the field. Alternatively,
some of us believe that the first step is to show the differences in opinion or point ofview, educating the user and allowing her to see how the sources differ from each otheror even summarising each point of view.

3

Conclusions

Overall, the workshop was a great success according to various indicators: the large numberof submissions, the quality of the papers, the diversity of topics, the large number of attendees
and the high level of interactions in the various discussions throughout the workshop. Thisemphasises the interest of the community in revisiting news IR. It reassures us that there is awide scope for future work on improving current models to better serve more complex information needs or indeed improving the evaluation approaches to better reflect the user model.
Building on this, we aim to follow it up by another edition in 2017, either in ECIR 2017, in abigger conference such as SIGIR 2017, or as part of an evaluation framework such as CLEF.

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Acknowledgments
We thank all the PC members for their constructive reviews. A detailed list of their names
and affiliations can be found on the workshop’s website7 .

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