Evaluating the impact of posted advertisements on content sharing sites

dc.contributor.author	Τομάρας, Πέτρος	el
dc.contributor.author	Νταλιάνης, Κλήμης	el
dc.date.accessioned	2015-06-07T16:24:32Z
dc.date.available	2015-06-07T16:24:32Z
dc.date.issued	2015-06-07
dc.identifier.uri	http://hdl.handle.net/11400/15486
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S1877042815012549	en
dc.subject	Social media
dc.subject	User popularity
dc.subject	Κοινωνικά μέσα δικτύωσης
dc.subject	Δημοτικότητα χρήστη
dc.title	Evaluating the impact of posted advertisements on content sharing sites	en
heal.type	conferenceItem
heal.secondaryTitle	an unsupervised social computing approach	en
heal.generalDescription	Procedia - Social and Behavioral Sciences. vol. 175. pp. 219-226.	en
heal.classification	Economics
heal.classification	Marketing
heal.classification	Οικονομία
heal.classification	Μάρκετινγκ
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85040850
heal.classificationURI	http://skos.um.es/unescothes/C02413
heal.classificationURI	N/A-Οικονομία
heal.classificationURI	N/A-Μάρκετινγκ
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2006007023
heal.identifier.secondary	doi:10.1016/j.sbspro.2015.01.1194
heal.language	en
heal.access	free
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Διοίκησης και Οικονομίας. Τμήμα Εμπορίας και Διαφήμισης.	el
heal.publicationDate	2015-02
heal.bibliographicCitation	Tomaras, P. and Ntalianis, K. (2014). Evaluating the impact of posted advertisements on content sharing sites: an unsupervised social computing approach. In the proceedings of the 3rd International Conference on Strategic Innovative Marketing (IC-SIM 2014). Madrid, Spain, 1st-4th September 2014. Elsevier Ltd: 2015. Available from: http://www.sciencedirect.com/science/article/pii/S1877042815012549 [Accessed 03/03/2015]	en
heal.abstract	During the last decade social media have greatly flourished, reaching rapidly the amazing figures of today. According to the Search Engine Journal (http://www.searchenginejournal.com/25-insane-social-media-facts/79645/): (a) currently 684,478 pieces of content are shared on Facebook every minute, (b) people are spending 1 out of every 7 minutes on Facebook when online, (c) 93% of marketers are using social media, however, only 9% of marketing companies have full-time bloggers and (d) around 46% of web users will look towards social media when making a purchase. It is obvious that businesses are tapping into social media, since they find them as a rich source of information and a business execution platform for product design and innovation, consumer and stakeholder relations management, and marketing. For this reason it is very useful to evaluate the impact of each posted advertisement. Towards this direction several supervised works have been presented in literature mainly focusing on traditional media. However, the impact of advertisements on new media (such as social networks, blogs etc.) has not been studied thoroughly yet. Additionally unsupervised impact evaluation is a very challenging problem. In this paper a novel unsupervised social computing approach is proposed that effectively performs both on open social media (twitter, blogs, microblogs etc) and on rule-stringent media (e.g. Facebook, LinkedIn etc). Our scheme algorithmically estimates the importance of each advertisement by considering both explicit interactions between advertisements and social media users and users’ popularity. The proposed method operates without human intervention and training and it is applied on real content posted on social media. Experimental results provide an insight of the performance of our system and specific areas are detected for future research.	en
heal.publisher	Elsevier Ltd	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference on Strategic Innovative Marketing (IC-SIM 2014)	en
heal.conferenceItemType	full paper