On the use of distributed semantics of tweet metadata for user age prediction

Pandya, Abhinay; Oussalah, Mourad; Monachesi, Paola; Kostakos, Panos

doi:10.1016/j.future.2019.08.018

Cited by 29 publications

(40 citation statements)

References 24 publications

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“…The input data for this MVNN came from nine attributes publicly available in the data of each tweet (see Table 5), thus entailing a rather more complex process than in Study 1. Using this sort of data to estimate the age of individuals following and engaging with internet content is in line with a body of previous literature (e.g., Morgan-Lopez et al 2017;Pandya et al 2020;Rao et al 2010).…”

Section: Methodssupporting

confidence: 83%

“Get a £10 Free Bet Every Week!”—Gambling Advertising on Twitter: Volume, Content, Followers, Engagement, and Regulatory Compliance

Rossi¹,

Nairn²,

Smith³

et al. 2021

Journal of Public Policy & Marketing

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The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

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Section: Methodssupporting

confidence: 83%

“Get a £10 Free Bet Every Week!”—Gambling Advertising on Twitter: Volume, Content, Followers, Engagement, and Regulatory Compliance

Rossi¹,

Nairn²,

Smith³

et al. 2021

Journal of Public Policy & Marketing

View full text Add to dashboard Cite

show abstract

“…The first model is based on textual information posted by users on Twitter and the other one is based on the category of photos that users have posted on Pinterest. Pandya et al [25] use the contents of the URLs and hashtags that are used in the tweets to classify users' age .…”

Section: Related Workmentioning

confidence: 99%

“…Various studies have shown that users with different ages and gender interact with content production systems differently [15,25]. Therefore, it can be efficient to identify the users' demographics to provide content closer to their preferences.…”

Section: Conclusion and Future Studymentioning

confidence: 99%

“…It is necessary, then, to identify and estimate the user's demographic based on his/her interactions [13]. Users demographic information have been identified based on posts they publish and sentiment analysis on the posts [25], and web browsing patterns [15]. Also, obtaining such information about users will enable recommender systems to provide more relevant contents to their users with higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting user demographics based on interest analysis

Shafiloo¹,

Kaedi²,

Pourmiri³

2021

Preprint

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These days, due to the increasing amount of information generated on the web, most web service providers try to personalize their services. Users also interact with web-based systems in multiple ways and state their interests and preferences by rating the provided items. In this paper, we propose a framework to predict users' demographic based on ratings registered by users in a system. To the best of our knowledge, this is the first time that the item ratings are employed for users' demographic prediction problem, which has extensively been studied in recommendation systems and service personalization. We apply the framework to Movielens dataset's ratings and predict users' age and gender. The experimental results show that using all ratings registered by users improves the prediction accuracy by at least 16% compared with previously studied models. Moreover, by classifying the items as popular and unpopular, we eliminate ratings belong to 95% of items and still reach an acceptable level of accuracy. This significantly reduces update cost in a timevarying environment. Besides this classification, we propose other methods to reduce data volume while keeping the predictions accurate.

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“…is information is particularly relevant in the customization of user-oriented websites (advertisements, news referrals, etc.). Next, the work presented in [3] addresses the age prediction problem by combining social media-specific metadata and languagerelated features. To accomplish this task, the authors combine (i) part-of-speech N-gram features, (ii) stylometry features (average sentence length, average word length, etc.…”

mentioning

confidence: 99%