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CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks
Univ Trento, Trento, Italy..
Univ Trento, Trento, Italy..
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0002-0264-8762
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Effectively predicting whether a given post or tweet is going to become viral in online social networks is of paramount importance for several applications, such as trend and break-out forecasting. While several attempts towards this end exist, most of the current approaches rely on features extracted from the underlying network structure over which the content spreads. Recent studies have shown, however, that prediction can be effectively performed with very little structural information about the network, or even with no structural information at all. In this study we propose a novel network-agnostic approach called CAS2VEC, that models information cascades as time series and discretizes them using time slices. For the actual prediction task we have adopted a technique from the natural language processing community. The particular choice of the technique is mainly inspired by an empirical observation on the strong similarity between the distribution of discretized values occurrence in cascades and words occurrence in natural language documents. Thus, thanks to such a technique for sentence classification using convolutional neural networks, CAS2VEC can predict whether a cascade is going to become viral or not. We have performed extensive experiments on two widely used real-world datasets for cascade prediction, that demonstrate the effectiveness of our algorithm against strong baselines.

Place, publisher, year, edition, pages
IEEE , 2018. p. 72-79
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-252430DOI: 10.1109/SNAMS.2018.8554730ISI: 000466979100012Scopus ID: 2-s2.0-85060045802OAI: oai:DiVA.org:kth-252430DiVA, id: diva2:1337511
Conference
2018 FIFTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS)
Note

QC 20190715

Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-07-15Bibliographically approved

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Bahri, LeilaSoliman, AmiraGirdzijauskas, Sarunas

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