-
Crestani, F.; Dominich, S.; Lalmas, M.; Rijsbergen, C.J.K. van: Mathematical, logical, and formal methods in information retrieval : an introduction to the special issue (2003)
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- Abstract
- Research an the use of mathematical, logical, and formal methods, has been central to Information Retrieval research for a long time. Research in this area is important not only because it helps enhancing retrieval effectiveness, but also because it helps clarifying the underlying concepts of Information Retrieval. In this article we outline some of the major aspects of the subject, and summarize the papers of this special issue with respect to how they relate to these aspects. We conclude by highlighting some directions of future research, which are needed to better understand the formal characteristics of Information Retrieval.
- Date
- 22. 3.2003 19:27:36
- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.4, S.281-284
-
Arapakis, I.; Lalmas, M.; Cambazoglu, B.B.; MarcosM.-C.; Jose, J.M.: User engagement in online news : under the scope of sentiment, interest, affect, and gaze (2014)
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- Abstract
- Online content providers, such as news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, through an analysis of a news article collection obtained from Yahoo News US, we demonstrate that news articles exhibit considerable variation in terms of the sentimentality and polarity of their content, depending on factors such as news provider and genre. Moreover, through a laboratory study, we observe the effect of sentimentality and polarity of news and comments on a set of subjective and objective measures of engagement. In particular, we show that attention, affect, and gaze differ across news of varying interestingness. As part of our study, we also explore methods that exploit the sentiments expressed in user comments to reorder the lists of comments displayed in news pages. Our results indicate that user engagement can be anticipated predicted if we account for the sentimentality and polarity of the content as well as other factors that drive attention and inspire human curiosity.
- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.10, S.1988-2005
-
Nikolov, D.; Lalmas, M.; Flammini, A.; Menczer, F.: Quantifying biases in online information exposure (2019)
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- Abstract
- Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."
- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.3, S.218-229
-
Arapakis, I.; Cambazoglu, B.B.; Lalmas, M.: On the feasibility of predicting popular news at cold start (2017)
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- Abstract
- Prominent news sites on the web provide hundreds of news articles daily. The abundance of news content competing to attract online attention, coupled with the manual effort involved in article selection, necessitates the timely prediction of future popularity of these news articles. The future popularity of a news article can be estimated using signals indicating the article's penetration in social media (e.g., number of tweets) in addition to traditional web analytics (e.g., number of page views). In practice, it is important to make such estimations as early as possible, preferably before the article is made available on the news site (i.e., at cold start). In this paper we perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News, a major news provider. We characterize the popularity of news articles through a set of online metrics and try to predict their values across time using machine learning techniques on a large collection of features obtained from various sources. Our findings indicate that predicting news popularity at cold start is a difficult task, contrary to the findings of a prior work on the same topic. Most articles' popularity may not be accurately anticipated solely on the basis of content features, without having the early-stage popularity values.
- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1149-1164
-
Kazai, G.; Lalmas, M.; Fuhr, N.; Gövert, N.: ¬A report an the first year of the INitiative for the Evaluation of XML Retrieval (INEX'02) (2004)
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- Source
- Journal of the American Society for Information Science and Technology. 55(2004) no.6, S.551-556
-
Blanke, T.; Lalmas, M.; Huibers, T.: ¬A framework for the theoretical evaluation of XML retrieval (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2463-2473
-
Ruthven, T.; Lalmas, M.; Rijsbergen, K.van: Incorporating user research behavior into relevance feedback (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.6, S.528-548
-
Piwowarski, B.; Amini, M.R.; Lalmas, M.: On using a quantum physics formalism for multidocument summarization (2012)
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- Source
- Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.865-888
-
Arapakis, I.; Lalmas, M.; Ceylan, H.; Donmez, P.: Automatically embedding newsworthy links to articles : from implementation to evaluation (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.1, S.129-145
-
Lehmann, J.; Castillo, C.; Lalmas, M.; Baeza-Yates, R.: Story-focused reading in online news and its potential for user engagement (2017)
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- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.4, S.869-883
-
Goyal, N.; Bron, M.; Lalmas, M.; Haines, A.; Cramer, H.: Designing for mobile experience beyond the native ad click : exploring landing page presentation style and media usage (2018)
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- Source
- Journal of the Association for Information Science and Technology. 69(2018) no.7, S.913-923
-
Ruthven, I.; Lalmas, M.; Rijsbergen, K. van: Combining and selecting characteristics of information use (2002)
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- Source
- Journal of the American Society for Information Science and technology. 53(2002) no.5, S.378-396
