Discovering Social Events through Online Attention

Kenett, Dror Y.; Stanley, H. Eugene; Liu, Huan

doi:10.1371/journal.pone.0102001

Cited by 11 publications

(10 citation statements)

References 32 publications

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“…This observation has been verified in recent years in a variety of social data sets, going from mobile phone [7] to online social media [8][9][10]. In addition, deviations from these cycles can help at detecting atypical events such as responses to catastrophes [1,14,15]. Fig.…”

Section: Data Collection and Basic Overviewmentioning

confidence: 67%

“…Discussions on protests [12,13] and political elections, advertisement of new products in marketing, announcements of scientific innovations [1], panic events such as earthquakes [14], and comments on TV shows are some examples where hashtags are widely used. Additionally, hashtags can be even used to track and locate crisis [15] and can spread under the influences of both endogeneous factors, that is the propagation between Twitter users following each others, and exogeneous sources such as TV and newspapers [16].…”

Section: Introductionmentioning

confidence: 99%

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Local Variation of Hashtag Spike Trains and Popularity in Twitter

Sanli

Lambiotte

2015

PLoS ONE

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We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media.

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Section: Data Collection and Basic Overviewmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Local Variation of Hashtag Spike Trains and Popularity in Twitter

Sanli

Lambiotte

2015

PLoS ONE

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“…Stock market moves have also been shown to be predictable early on by the number of views of Wikipedia articles related to financial subjects [ 22 ] and by data from Google Trends [ 23 ]. Similarly, Twitter discussions have helped locate political demonstrations [ 24 ]. Thus, the Internet has become a crucial medium for gathering information [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

I Like, I Cite? Do Facebook Likes Predict the Impact of Scientific Work?

2015

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Due to the increasing amount of scientific work and the typical delays in publication, promptly assessing the impact of scholarly work is a huge challenge. To meet this challenge, one solution may be to create and discover innovative indicators. The goal of this paper is to investigate whether Facebook likes for unpublished manuscripts that are uploaded to the Internet could be used as an early indicator of the future impact of the scientific work. To address our research question, we compared Facebook likes for manuscripts uploaded to the Harvard Business School website (Study 1) and the bioRxiv website (Study 2) with traditional impact indicators (journal article citations, Impact Factor, Immediacy Index) for those manuscripts that have been published as a journal article. Although based on our full sample of Study 1 (N = 170), Facebook likes do not predict traditional impact indicators, for manuscripts with one or more Facebook likes (n = 95), our results indicate that the more Facebook likes a manuscript receives, the more journal article citations the manuscript receives. In additional analyses (for which we categorized the manuscripts as psychological and non-psychological manuscripts), we found that the significant prediction of citations stems from the psychological and not the non-psychological manuscripts. In Study 2, we observed that Facebook likes (N = 270) and non-zero Facebook likes (n = 84) do not predict traditional impact indicators. Taken together, our findings indicate an interdisciplinary difference in the predictive value of Facebook likes, according to which Facebook likes only predict citations in the psychological area but not in the non-psychological area of business or in the field of life sciences. Our paper contributes to understanding the possibilities and limits of the use of social media indicators as potential early indicators of the impact of scientific work.

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“…Our main aim was to explore and investigate the structural nature of the collaboration network using methods of network science [60] , [61] , as have been used before successfully to study scientific production and consumption in physics [62] , [63] , the interaction of people in online affiliation networks [64] the discovery of social events through online attention [65] and the identification of influential spreaders in complex networks [66] . Assessing the fit of different pattern models to the observed network and analyzing how the collaboration ties are patterned, we assume, were substantive ways of learning how Romanian sociologists interact, how ideas are shared and how research practices in the field are influenced and adjusted.…”

Section: Discussionmentioning

confidence: 99%

Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network

Hâncean

Perc

2014

PLoS ONE

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Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

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Discovering Social Events through Online Attention

Cited by 11 publications

References 32 publications

Local Variation of Hashtag Spike Trains and Popularity in Twitter

Local Variation of Hashtag Spike Trains and Popularity in Twitter

I Like, I Cite? Do Facebook Likes Predict the Impact of Scientific Work?

Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network

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