The influence of network structure and prosocial cultural norms on charitable giving

A multilevel analysis of Movember’s fundraising campaigns in 24 countries

by Tijs van den Broek, Ariana Need, Michel Ehrenhard, Anna Priante, and Djoerd Hiemstra

This study examines how the interplay between an online campaign’s network structure and prosocial cultural norms in a country affect charitable giving. We conducted a multilevel analysis that includes Twitter network and aggregated donation data from the 2013 Movember fundraising campaigns in 24 countries during 62 campaign days. Prosocial cultural norms did not affect the relationship between network size and average donations raised, nor did they affect the relationship between network centralization and average donation amount. Prosocial cultural norms did affect the relationship between network density and average donations raised. However, this effect was negative and contrary to our expectation.

Published in Social Networks 58, pages 128-135

The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey

by Muhammad Nakhaee, Djoerd Hiemstra, Mariëlle Stoelinga, and Martijn van Noort

Railway systems play a vital role in the world’s economy and movement of goods and people. Rail tracks are one of the most critical components needed for the uninterrupted operation of railway systems. However, environmental conditions or mechanical forces can accelerate the degradation process of rail tracks. Any fault in rail tracks can incur enormous costs or even result in disastrous incidents such as train derailment. Over the past few years, the research community has adopted the use of machine learning (ML) algorithms for diagnosis and prognosis of rail defects in order to help the railway industry to carry out timely responses to failures. In this paper, we review the existing literature on the state-of-the-art machine learning-based approaches used in different rail track maintenance tasks. As one of our main contributions, we also provide a taxonomy to classify the existing literature based on types of methods and types of data. Moreover, we present the shortcomings of current techniques and discuss what research community and rail industry can do to address these issues. Finally, we conclude with a list of recommended directions for future research in the field.

To be presented at the International Conference on Reliability, Safety and Security of Railway Systems: Modeling, Analysis, Verification and Certification (RSSRail 2019) on 4-6 June 2019 in Lille, France.

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Wim Florijn graduates on Semantically Grouping Search Query Data

Information Retrieval by Semantically Grouping Search Query Data

by Wim Florijn

Query data analysis is a time-consuming task. Currently, a method exists where word (combinations) in queries are labelled by using an information collection consisting of regular expressions. Because the information collection does not contain regular expressions from never-before seen domains, the method heavily relies on manual work, resulting in decreased scalibility. Therefore, a machine-learning based method is proposed in order to automate the annotation of word (combinations) in queries. This research searches for the optimal configuration of a pre-processing method, word embedding model, additional data set and classifier variant. All configurations have been examined on multiple data sets, and appropriate performance metrics have been calculated. The results show that the optimal configuration consists of omitting pre-processing, training a fastText model and enriching word features using additional data in combination with a recurrent classifier. We found that an approach using machine learning is able to obtain excellent performance on the task of labelling word (combinations) in search queries.

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Anna Priante defends PhD thesis on online social movement campaigns

by Anna Priante

Social movement organizations widely use social media to organize collective action for social change, such as cancer awareness campaigns. However, little is known about how effective online social movement campaigns are at generating social change by translating online action into meaningful (offline) action. This dissertation examines the micro-mobilization dynamics at play that can explain the effectiveness of online social social movement campaigns. This book comprises seven chapters presenting research based on a multidisciplinary, mixed-method approach, combining theories and methods from sociology, social psychology, communication science, and computational social science. The findings show that, with mobilization dynamics of collective action, we can gain an important understanding of the mechanisms at work during online social movement campaigns and of the effectiveness of such campaigns in fostering communication processes related to the cause, obtaining important resources for the cause, developing a collective identity, and raising awareness.

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Anna’s defense was the 5000th PhD defense at the UT!

Marieke Graef graduates cum laude on the Analysis of HPV discussions on Twitter

Responses to HPV Vaccination Campaigns in The Netherlands: an analysis of discussions on Twitter

by Marieke Graef

Even though the human papillomavirus vaccine (HPV) is an effective and safe instrument to decrease HPV infections and cases of several types of cancer, the Dutch HPV vaccination rate has been suboptimal from the start and has even shown a decline in the last two years. This study sought to assess the determinants of HPV vaccination uptake in the Netherlands and how the vaccine and RIVM and GGDTwente messages were discussed on Twitter from 2011 till 2016. Method: All Dutch language tweets mentioning HPV from the years 2011 till 2016 were collected from a database, amounting to a total of 17319. A content analysis of all tweets was carried out manually. The content of the GGDTwente and RIVM tweets was examined as well as responses to these tweets. Furthermore, the tweets were analyzed for specific determinants of HPV vaccination uptake and general sentiments. Results: The GGDTwente and RIVM only became truly active on Twitter regarding the HPV vaccination program in 2015. The RIVM tweets received significantly more response, though this response mostly consisted of retweets. Nearly all GGDTwente tweets concerned vaccination schedules. By far the most common determinant of low vaccination uptake in tweets from the public was the fear of side-effects, with scare stories going viral in 2015 and 2016 especially. On the other hand, publications on the high number of HPV infections among women received a lot of attention as well. Overall, the general sentiment towards the HPV vaccine on Twitter was more positive than negative in the first years, but due to stories about side-effects turned more negative in 2015. Conclusions: The results show that the fear of side-effects is something that needs to be addressed by public health authorities. Additionally, more practical measures such as a school-based vaccination program may be a great way to help increase the vaccination rate.

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Flávio Martins defends PhD thesis on Temporal Models for Microblog Search

Temporal Information Models for Real-Time Microblog Search

by Flávio Martins

Real-time search in Twitter and other social media services is often biased towards the most recent results due to the “in the moment” nature of topic trends and their ephemeral relevance to users and media in general. However, “in the moment”, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where it was shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches:
(1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time;
(2) efficient methods for federated query expansion towards the improvement of query meaning; and
(3) exploiting multiple sources towards the detection of temporal query intent.
It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step.

(Photo by @krisztianbalog@twitter.com)

Whom to Follow on Mastodon?

Recommending Users: Whom to Follow on Federated Social Networks

by Jan Trienes, Andrés Torres Cano, and Djoerd Hiemstra

To foster an active and engaged community, social networks employ recommendation algorithms that filter large amounts of contents and provide a user with personalized views of the network. Popular social networks such as Facebook and Twitter generate follow recommendations by listing profiles a user may be interested to connect with. Federated social networks aim to resolve issues associated with the popular social networks – such as large-scale user-surveillance and the miss-use of user data to manipulate elections – by decentralizing authority and promoting privacy. Due to their recent emergence, recommender systems do not exist for federated social networks, yet. To make these networks more attractive and promote community building, we investigate how recommendation algorithms can be applied to decentralized social networks. We present an offline and online evaluation of two recommendation strategies: a collaborative filtering recommender based on BM25 and a topology-based recommender using personalized PageRank. Our experiments on a large unbiased sample of the federated social network Mastodon shows that collaborative filtering approaches outperform a topology-based approach, whereas both approaches significantly outperform a random recommender. A subsequent live user experiment on Mastodon using balanced interleaving shows that the collaborative filtering recommender performs on par with the topology-based recommender.

This paper will be presented at the 17th Dutch-Belgian Information Retrieval workshop in Leiden on 23 November 2018

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Participate in the Dutch-Belgian Information Retrieval Workshop

The The 17th Dutch-Belgian Information Retrieval workshop (DIR 2018) takes place in Leiden on 23 November 2018. DIR has a diverse 1-day programme with 2 keynotes, 5 talks, 7 posters and 4 demos!

The Dutch-Belgian Information Retrieval workshop (DIR) aims to serve as an international platform (with a special focus on the Netherlands and Belgium) for exchange and discussions on research & applications in the field of information retrieval and related fields.

More information at: http://dir2018.nl.

Call for ECIR short papers and posters

The European Conference on Information Retrieval (ECIR) is the prime European forum for the presentation of original research in the field of Information Retrieval. ECIR 2019 is seeking high-quality and original submissions on theory, experimentation, and practice regarding the retrieval, representation, management, and usage of textual, visual and multi-modal information. ECIR strongly supports user, system, application, and evaluation focused papers:

  • User aspects including information interaction, contextualisation, personalisation, simulation, characterisation, and information behaviours.
  • System aspects including retrieval and recommendation algorithms, machine learning, deep learning, content representation, natural language processing, system architectures, and efficiency methods.
  • Applications such as search and recommender systems, web and social media apps, domain specific search (professional, bio, chem, etc.), novel interfaces, intelligent search agents/bots, and related innovative search tools.
  • Evaluation research including new measures and novel methods for the measurement and evaluation of users, systems and/or applications.

In addition to these traditional topic areas, ECIR 2019 will be encouraging the submissions of papers on:

  • New and Emerging Applications of IR including eHealth, precision medicine, early risk prediction, incident streams, digital text forensics, cultural and social informatics, life and biodiversity retrieval, living lab evaluations, conversational and intelligent search agents, and search as learning.

The Short Paper Track calls for original contributions presenting novel, thought-provoking ideas and addressing innovative application areas within the field of Information Retrieval. The inclusion of promising (preliminary) results is encouraged but not required. Papers that stimulate and promote discussion are particularly encouraged. Short paper submissions should be 6 pages in length plus additional pages for references.