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Shapira, Bracha; Shoval, Peretz; Tractinsky, Noam; Meyer, Joachim

doi:10.1002/asi.v60:11

Cited by 208 publications

(13 citation statements)

References 439 publications

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“…quality, will be quantified later) which cannot be reflected merely by the number of citations, since citation counts only reflect the popularity, but not the quality or prestige [18]. Inspired by the success of Google's ranking system for web pages, the popular algorithm PageRank as well as some of its variants have been used to show the prestige in citation networks of journals [19], publications [20][21][22] and scientists [15,23]. Since the network analyzed is a particular projection of a citation network, the result depends on how weights are assigned to links.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity

Zhou¹,

Lü²,

Li³

2012

New J. Phys.

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The number of citations is a widely used metric for evaluating the scientific credit of papers, scientists and journals. However, it so happens that papers with fewer citations from prestigious scientists have a higher influence than papers with more citations. In this paper, we argue that by whom the paper is being cited is of greater significance than merely the number of citations. Accordingly, we propose an interactive model of author-paper bipartite networks as well as an iterative algorithm to obtain better rankings for scientists and their publications. The main advantage of this method is twofold: (i) it is a parameter-free algorithm; (ii) it considers the relationship between the prestige of scientists and the quality of their publications. We conducted real experiments on publications in econophysics, and used this method to evaluate the influence of related scientific journals. The comparison between the rankings by our method and simple citation counts suggests that our method is effective in distinguishing prestige from popularity.

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Section: Introductionmentioning

confidence: 99%

Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity

Zhou¹,

Lü²,

Li³

2012

New J. Phys.

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show abstract

“…Finally, they noted that content lifespan is very important to virality, as also mentioned by Ienco and Bonchi Castillo (2010). In the same OSN (Twitter), Sakaki et al (2010) studied users as social sensors by monitoring information flow and dissemination during earthquake incidents. In their approach, they showed that earthquake detection by monitoring information flow from tweets (e.g.…”

Section: Related Workmentioning

confidence: 89%

Lifespan and propagation of information in On-line Social Networks: A case study based on Reddit

Haralabopoulos

Anagnostopoulos

Zeadally

2015

Journal of Network and Computer Applications

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“…The tag relevance estimations are computed as the stationary or the convergence probabilities of a random walk processes. Notable example of this approach is the PageRank algorithm (Ding et al, 2009;Koschützki et al, 2008;Junker et al, 2008). Another group of methods exploit background knowledge (such as the lexical database Wordnet and a massive corpus indexed by Google) to perform the refinement of the image annotations (Jin et al, 2010;Wang et al, 2007).…”

Section: Related Workmentioning

confidence: 99%

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

Gligorov

Hildebrand

Ossenbruggen

et al. 2017

HCj

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Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. One such GWAP is Waisda?, a video labeling game where players tag streaming video and win points by reaching consensus on tags with other players. The open-ended and unconstrained manner of tagging in the fast-paced setting of the game has fundamental impact on the resulting tags. Consequently, the Waisda? tags predominately describe visual objects and rarely refer to the topics of the videos. In a previous study [ECIR 2013, 50-61], Gligorov at al. showed that the Waisda? tags are effective in finding video segments that depict a specific entity (person, object, etc.) of interest. This study evaluates the performance of the game tags for retrieval of videos that are about a given topic. To this end, we setup a Cranfield-style experiment for which we use an evaluation dataset that consists of: (i) a collection of videos tagged in Waisda?, (ii) a set of queries derived from real-life query logs, and (iii) relevance judgements. While we reuse the collection of videos and the set of queries compiled by Gligorov at al., we designed the set of relevance judgments specifically for this study. The novelty aspects of this paper are as follows. Our results demonstrate that the raw, unprocessed game tags are not well suited for retrieving videos based on topic. We perform a qualitative analysis of the search results which reveals that this is mainly caused by the presence of general tags which refer to visual objects unrelated to the topics of the video. Thus, we characterize the quality of the game tags as topical annotations in order to detect and filter out the non-topical ones. We explore several features of the game tags which could serve as an indication of their quality as topical descriptors. Our results show that after filtering, the game tags perform equally well compared to the manually crafted metadata when it comes to accessing the videos based on topic. An important consequence of this finding is that tagging games can provide a cost-effective alternative in situations when manual annotation by professionals is too costly.

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Untitled

Cited by 208 publications

References 439 publications

Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity

Quantifying the influence of scientists and their publications: distinguishing between prestige and popularity

Lifespan and propagation of information in On-line Social Networks: A case study based on Reddit

Topical Video Search: Analysing Video Concept Annotation through Crowdsourcing Games

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