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 @firstname.lastname@example.org)
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
Understanding engagement behavior in online brand communities : how social identity relates to frequency of interaction and tweet sentiment.
by Candy Reebroek
This study explains engagement behavior in online brand communities based on data of Twitter users who present different types of social identities. For this, we examined fifteen online brand communities that are popular on Twitter and originated from fashion, fast-food, gaming, cars, and sports sectors. In total, 27,143 twitter messages were analyzed from 22,333 unique Twitter users. We used the Twitter user’s profile descriptions to classify their social identity with the help of computational methods such as Machine Learning and Natural Language Processing. To study the engagement behavior of the Twitter users, we calculated the tweets sentiment and the frequency of interaction between Twitter users and online brand communities. We found that tweet sentiment and frequency of interaction vary significantly between different social identity groups when mentioning different online brand communities. This result is important for online brand community managers to understand what kind of Twitter users interact with their online brand community and how these users engage with the community. Right now, they might only investigate demographics about the users but do not consider the user’s self-presentation online. Furthermore, we made a theoretical contribution by including a larger dataset, by performing computational methods and by exploring multiple online brand communities from different sectors.
The role of Online Identity on Donations to Nonprofit Organizations in Online Health Campaigns
by Anna Priante, Ariana Need, Tijs van den Broek, and Djoerd Hiemstra
Nonprofit Organizations largely use social media to mobilize people for social causes and encourage participation in collective action, such as advocacy campaigns. However, little is known about the micro-level mechanisms that drive individual mobilization outcomes that require a substantial effort in participation such as collecting donations during advocacy campaigns. By answering the call to combine motivational and structural factors that explain the mechanisms driving people’s engagement in collective action via social media, we focus on the role of online social identity as a motivator to engage in campaigns, and on individual network positions as opportunity structures for online mobilization. Using the 2014 US Movember health movement campaign on Twitter as an empirical context, we adopt a multi-method approach combining Natural Language Processing, social network analysis and multivariate regression analysis to investigate the effects of online social identity and structural network position on the amount of collected donations for medical research during campaign. We find that only social identities related to occupations and professions have significant effects on the amount of collected donation, whereas network position matters when movement members are central in the communication process because they connect different cohesive subgroups, or communities of the network, characterized by the prevalence of weak ties. We show the importance of integrating the study of identity and network to advance our understanding of online micro-mobilization dynamics. This study offers contributions to research at the intersection of research on the non-profit sector, social movements, media and communication, and health fundraising.
To be presented at the 78th Annual Meeting of the Academy of Management on 14 August 2018 in Chicago, USA
How Online Identity influences Collected Donations in Online Health Campaigns
by Anna Priante, Michel Ehrenhard, Tijs van der Broek, Ariana Need, Djoerd Hiemstra
Health advocacy organizations increasingly use social media to engage people in fundraising campaigns for medical research, such as cancer prevention. However, little is known about the effectiveness of online health campaigns and the psychosocial mechanisms that drive people’s voluntary engagement to collect money for medical research. By using identity-based motivation theory from social psychology, we focus on campaign participants’ online occupational identity, such as being a doctor, and how it provides motivation to collect donations. We investigate the mechanisms, such as fundraisers’ Twitter activity as a cognitive process and their central network positions in online communication, that mediate the relationship between identity and donations.
We adopt a multi-method approach combining automatic text analysis, Natural Language Processing from computational linguistics, social network analysis and multivariate regression analysis. Using the 2014 US Movember health movement campaign on Twitter as an empirical context, we find that when people are engaged in health fundraising on Twitter, their success depends on the extent to which they act in occupational identity-congruent ways. In addition, we find that fundraisers’ Twitter activity as a sense-making, cognitive process – and not their central positions in online communication – mediates the relation between identity and donations.
We show the importance of integrating both people’s social identification and cognitive processes into theory and research for a better understanding of how occupational identity matters in online health campaigns. This study offers contributions to research at the intersection of health advocacy, social media use, and, more broadly, online social movements. We conclude by discussing the practical implications of these findings for health advocacy organizations.
To be presented at the 113th Annual Meeting of the American Sociological Association
(ASA 2018) on 11-14 August 2018 in Philadelphia, USA.
The University of Twente is the first Dutch university to run its own Mastodon server. Mastodon is a social network based on open web protocols and free, open-source software. It is decentralized like e-mail. Learning from failures of other networks, Mastodon aims to make ethical design choices to combat the misuse of social media. By joining U. Twente Mastodon, you join a global social network with more than a million people. The university will not sell your data, nor show you advertisements. Mastodon U. Twente is available to all students, alumni, and employees.
Join Mastodon U. Twente now
Cross-Domain Authorship Attribution as a Tool for Digital Investigations
by Christel Geurts
On the darkweb sites promoting illegal content are abundant and new sites are constantly created. At the same time Law Enforcement is working hard to take these sites down and track down the persons involved. Often, after taking down a site, users change their name and move to a different site. But what if Law Enforcement could track users across sites? Different sites or sources of information are called a domain. As the domain changes, often the context of a message also changes, making it challenging to track users simply on words used. The aim of this thesis is to develop a system that can link written text of authors in a cross-domain setting. The system was tested on a blog corpus and verified on police data. Tests show that multinomial logistic regression and Support Vector Machines with a linear kernel perform well. Character 3-grams work well as features, combining multiple feature sets increases performance. Tests show that Logistic Regression models with a combined feature set performed best (accuracy = 0.717, MRR = 0.7785, 1000 authors (blog corpus)). On the police data the Logistic Regression model had an accuracy of 0.612 and a MRR of 0.6883 for 521 authors.
by Slavica Zivanovic
There is an ongoing discussion about the applicability of social media data in scientific research. Moreover, little is known about the feasibility to use these data to capture the Quality of Life (QoL). This study explores the use of social media in QoL research by capturing and analysing people’s perceptions about their QoL using Twitter messages. The methodology is based on a mixed method approach, combining manual coding of the messages, automated classification, and spatial analysis. The city of Bristol is used as a case study, with a dataset containing 1,374,706 geotagged Tweets sent within the city boundaries in 2013. Based on the manual coding results, health, transport, and environment domains were selected to be further analysed. Results show the difference between Bristol wards in number and type of QoL perceptions in every domain, spatial distribution of positive and negative perceptions, and differences between the domains. Furthermore, results from this study are compared to the official QoL survey results from Bristol, statistically and spatially. Overall, three main conclusions are underlined. First, Twitter data can be used to evaluate QoL. Second, based on people’s opinions, there is a difference in QoL between Bristol neighbourhoods. And, third, Twitter messages can be used to complement QoL surveys but not as a proxy. The main contribution of this study is in recognising the potential Twitter data have in QoL research. This potential lies in producing additional knowledge about QoL that can be placed in a planning context and effectively used to improve the decision-making process and enhance quality of life of residents.
by Marco Schultewolter
Often, software providers ask users to insert personal data in order to grant them the right to use their software. These companies want the user profile as correct as possible, but users sometimes tend to enter incorrect information. This thesis researches and discusses approaches to automatically verify this information using third-party web resources.
Therefore, a series of experiments is done. One experiment compares different similarity measures in the context of a German phone book directory for again different search approaches. Another experiment takes the approach to use a search engine without a specific predefined data source. Ways of finding persons in search engines and of extracting address information from unknown websites are compared in order to do so.
It is shown, that automatic verification can be done to some extent. The verification of name and address data using external web resources can support the decision with Jaro-Winkler as similarity measure, but it is still not solid enough to only rely on it. Extracting address information from unknown pages is very reliable when using a sophisticated regular expression. Finding persons on the internet should be done by using just the full name without any additions.
21-22 January 2016
University of Twente
If you're interested in social media analysis and/or computational social science, there will be interesting guest speakers, including speakers from UCLA, TNO, TU Delft, Greenpeace, Sanquin, and Twitter.