Wednesday, 11 May 2016

EAIMS: Emergency Analysis Identification and Management System

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Social media has great potential as a means to enable civil protection and law enforcement agencies to more effectively tackle disasters and emergencies. However, there is currently a lack of tools that enable civil protection agencies to easily make use of social media. The Emergency Analysis Identification and Management System (EAIMS) is a prototype service that provides real-time detection of emergency events, related information finding and credibility analysis tools for use over social media during emergencies. This system exploits machine learning over data gathered from past emergencies and disasters to build effective models for identifying new events as they occur, tracking developments within those events and analyzing those developments for the purposes of enhancing the decision making processes of emergency response agencies.

Richard McCreadie, Craig Macdonald, and Iadh Ounis
EAIMS: Emergency Analysis Identification and Management System
In Proceedings of SIGIR, 2016.

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Tuesday, 8 March 2016

Comparing Overall and Targeted Sentiments in Social Media during Crises

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The tracking of citizens' reactions in social media during crises has attracted an increasing level of interest in the research community. In particular, sentiment analysis over social media posts can be regarded as a particularly useful tool, enabling civil protection and law enforcement agencies to more effectively respond during this type of situation. Prior work on sentiment analysis in social media during crises has applied well-known techniques for overall sentiment detection in posts. However, we argue that sentiment analysis of the overall post might not always be suitable, as it may miss the presence of more targeted sentiments, e.g. about the people and organizations involved (which we refer to as sentiment targets).  Through a crowdsourcing study, we show that there are marked differences between the overall tweet sentiment and the sentiment expressed towards the subjects mentioned in tweets related to three crises events.

Saul Vargas, Richard McCreadie, Craig Macdonald, and Iadh Ounis
Comparing Overall and Targeted Sentiments in Social Media during Crises
In Proceedings of ICWSM, 2016.

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Thursday, 16 April 2015

SUPER: Towards the use of Social Sensors for Security Assessments and Proactive Management of Emergencies

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Social media statistics during recent disasters (e.g. the 20 million tweets relating to `Sandy' storm and the sharing of related photos in Instagram at a rate of 10/sec) suggest that the understanding and management of real-world events by civil protection and law enforcement agencies could benefit from the effective blending of social media information into their resilience processes. In this paper, we argue that despite the widespread use of social media in various domains (e.g. marketing/branding/finance), there is still no easy, standardized and effective way to leverage different social media streams -- also referred to as social sensors -- in security/emergency management applications. We also describe the EU FP7 project SUPER (Social sensors for secUrity assessments and Proactive EmeRgencies management), started in 2014, which aims to tackle this technology gap.

Richard McCreadie, Karolin Kappler, Magdalini Kardara, Andreas Kaltenbrunner, Craig Macdonald, John Soldatos and Iadh Ounis
SUPER: Towards the use of Social Sensors for Security Assessments and Proactive Management of Emergencies
In Proceedings of SWDM 2015 (at WWW 2015).

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Crowdsourced Rumour Identification During Emergencies

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When a significant event occurs, many social media users leverage platforms such as Twitter to track that event. Moreover, emergency response agencies are increasingly looking to social media as a source of real-time information about such events. However, false information and rumours are often spread during such events, which can influence public opinion and limit the usefulness of social media for emergency management. In this paper, we present an initial study into rumour identification during emergencies using crowdsourcing. In particular, through an analysis of three tweet datasets relating to emergency events from 2014, we propose a taxonomy of tweets relating to rumours. We then perform a crowdsourced labeling experiment to determine whether crowd assessors can identify rumour-related tweets and where such labeling can fail. Our results show that overall, agreement over the tweet labels produced were high (0.7634 Fleiss kappa), indicating that crowd-based rumour labeling is possible. However, not all tweets are of equal difficulty to assess. Indeed, we show that tweets containing disputed/controversial information tend to be some of the most difficult to identify.  

Richard McCreadie, Craig Macdonald and Iadh Ounis.
Crowdsourced Rumour Identification During Emergencies
In Proceedings of RDSM 2015 (at WWW 2015).

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Monday, 29 September 2014

Incremental Update Summarization: Adaptive Sentence Selection based on Prevalence and Novelty

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The automatic summarization of long-running events from news steams is a challenging problem. A long-running event can contain hundreds of unique ‘nuggets’ of information to summarize, spread-out over its lifetime. Meanwhile, informatio reported about it can rapidly become outdated and is often highly redundant. Incremental update summarization (IUS) aims to select sentences from news streams to issue as updates to the user, summarising that event over time. The updates issued should cover all of the key nuggets concisely and before the information contained in those nuggets becomes outdated. Prior summarization approaches when applied to IUS can fail, since they define a fixed summary length that cannot effectively account for the different magnitudes and varying rate of development of such events. In this paper, we propose a novel IUS approach that adaptively alters the volume of content issued as updates over time with respect to the prevalence and novelty of discussions about the event. It incorporates existing state-of-the art summarization techniques to rank candidate sentences, followed by a supervised regression model that balances novelty, nugget coverage and timeliness when selecting sentences from the top ranks. We empirically evaluate our approach using the TREC 2013 Temporal Summarization dataset extended with additional assessments. Our results show that by adaptively adjusting the number of sentences to select over time, our approach can nearly double the performance of effective summarization baselines.

Richard McCreadie, Craig Macdonald and Iadh Ounis.
Incremental Update Summarization: Adaptive Sentence Selection based on Prevalence and Novelty
In Proceedings of CIKM 2014.

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Tuesday, 9 September 2014

A Study of Personalised Medical Literature Search

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Medical search engines are used everyday by both medical practitioners and the public to find the latest medical literature and guidance regarding conditions and treatments. Importantly, the information needs that drive medical search can vary between users for the same query, as clinicians search for content specific to their own area of expertise, while the public search about topics of interest to them. However, prior research into personalised search has so far focused on the Web search domain, and it is not clear whether personalised approaches will prove similarly effective in a medical environment. Hence, in this paper, we investigate to what extent personalisation can enhance medical search effectiveness. In particular, we first adapt three classical approaches for the task of personalisation in the medical domain, which leverage the user’s clicks, clicks by similar users and explicit/implicit user profiles, respectively. Second, we perform a comparative user study with users from the TRIPDatabase.com medical article search engine to determine whether they outperform an effective baseline production system. Our results show that search result personalisation in the medical domain can be effective, with users stating a preference for personalised rankings for 68% of the queries assessed. Furthermore, we show that for the queries tested, users mainly preferred personalised rankings that promote recent content clicked by similar users, highlighting time as a key dimension of medical article search.

Richard McCreadie, Craig Macdonald and Iadh Ounis.
A Study of Personalised Medical Literature Search
In Proceedings of CLEF 2014.

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Comparing Algorithms for Microblog Summarisation

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Event detection and tracking using social media and user-generated content has received a lot of attention from the research community in recent years, since such sources can purportedly provide up-to-date information about events as they evolve, e.g. earthquakes. Concisely reporting (summarising) events for users/emergency services using information obtained from social media sources like Twitter is not a solved problem. Current systems either directly apply, or build upon, classical summarisation approaches previously shown to be effective within the newswire domain. However, to-date, research into how well these approaches generalise from the newswire to the microblog domain is limited. Hence, in this paper, we compare the performance of eleven summarisation approaches using four microblog summarisation datasets, with the aim of determining which are the most effective and therefore should be used as baselines in future research. Our results indicate that the SumBasic algorithm and Centroid-based summarisation with redundancy reduction are the most effective approaches, across the four datasets and five automatic summarisation evaluation measures tested.

Stuart Mackie, Richard McCreadie, Craig Macdonald and Iadh Ounis.
Comparing Algorithms for Microblog Summarisation
In Proceedings of CLEF 2014.

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