In this article, we present PostGage, a new framework that identifies the most suitable publishers of target content on social media, based on the level of user engagement they are expected to generate. PostGage uses a network-based data model that considers the characteristics of publishers, as well as the degree to which the semantic content and lifespan of the posts to be published overlap with those of the posts already published. Leveraging this data model, PostGage builds a multinomial classifier based on a graph neural network (GNN). The alignment between the graph-based data model and the GNN technology allows a precise classification of publishers based on their potential to generate user engagement for target content. This feature enables a more precise definition of publishing strategies on social media, as it allows the selection of the most promising publishers for a given post. The network-based model, the post ageing mechanism and the adoption of the GNN as a multiclass classifier represent PostGage’s key contributions. We also present a series of tests performed on X that demonstrate the high quality of PostGage’s results (e.g. 92% precision in predicting high-engagement posts), establishing it as an invaluable tool for various applications, including marketing and social media management.
AlkhodairSADingSHHFungBCM, et al. Detecting breaking news rumors of emerging topics in social media. Inf Process Manag2020; 57(2): 102018.
2.
LuoYMaJYeoCK.Identification of rumour stances by considering network topology and social media comments. J Inf Sci2022; 48(1): 118–130.
3.
HuangSWangBLiX, et al. Industry 5.0 and Society 5.0 – comparison, complementation and co-evolution. J Manuf Syst2022; 64: 424–428.
4.
BeniicheARostamiSMaierM.Society 5.0: internet as if people mattered. IEEE Wirel Commun2022; 29(6): 160–168.
5.
ChoiEBKimJJeongD, et al. Detecting agro: Korean trolling and clickbaiting behaviour in online environments. J Inf Sci2023; 50: 3–16.
6.
XuXLuYVogel-HeuserB, et al. Industry 4.0 and Industry 5.0 – inception, conception and perception. J Manuf Syst2021; 61: 530–535.
7.
RowleyJEKeeganBJ.An overview of systematic literature reviews in social media marketing. J Inf Sci2020; 46(6): 725–738.
8.
BonifaziGCauteruccioFCorradiniE, et al. A multilayer network-based framework for handling and comparing user histories in X. J Inf Sci. Epub ahead of print 15 November 2024. DOI: 10.1177/01655515241281313.
9.
HuangJSharmaASunS, et al. Embedding-based retrieval in Facebook search. Proceedings of the international conference on knowledge discovery & data mining (SIGKDD’20), Online, 6–10 July 2020, pp. 2553–2561. New York: ACM.
10.
ShahbaznezhadHDolanRRashidiradM.The role of social media content format and platform in users’ engagement behavior. J Interact Mark2021; 53: 47–65.
11.
ReadWRobertsonNMcQuilkenL, et al. Consumer engagement on Twitter: perceptions of the brand matter. Eur J Mark2019; 53(9): 1905–1933.
12.
CauteruccioFCorradiniETerracinaG, et al. Investigating Reddit to detect subreddit and author stereotypes and to evaluate author assortativity. J Inf Sci2022; 48: 783–810.
13.
Di GangiPWaskoMW. Social media engagement theory: exploring the influence of user engagement on social media usage. J Organ End User Comput2016; 28(2): 53–73.
14.
McShaneLPancerEPooleM, et al. Emoji, playfulness, and brand engagement on Twitter. J Interact Mark2021; 53(1): 96–110.
15.
Diaz-AvilesELamHTPinelliF, et al. Emoji, playfulness, and brand engagement on Twitter. Proceedings of the recommender systems challenge (RecSys’14), Foster City, CA, 6–10 October 2014, pp. 41–46. New York: ACM.
16.
JaakonmäkiRRMüllerOVom BrockeJ.The impact of content, context, and creator on user engagement in social media marketing. In: Proceedings of the Hawaii international conference on system sciences (HICSS’17), Hilton Waikoloa Village, HI, 4–7 January 2017, pp. 1152–1160. Hawaii: AIS Electronic Library (AISeL).
17.
MagalhãesJPessoaRSouzaC, et al. A recommender system for predicting user engagement in Twitter. In: Proceedings of the recommender systems challenge (RecSys’14), Foster City, CA, 6–10 October 2014, pp. 52–57. New York: ACM.
18.
SilvaMCeschinFShresthaP, et al. Predicting misinformation and engagement in covid-19 twitter discourse in the first months of the outbreak. arXiv:2012.021642020.
19.
VolkovsMChengZRavautM, et al. Predicting twitter engagement with deep language models. In: Proceedings of the recommender systems challenge (RecSys’20), Online, 26 September 2020, pp. 38–43. New York: ACM.
20.
ArazziMCotogniMNoceraA, et al. Predicting tweet engagement with graph neural networks. In: Proceedings of the ACM international conference on multimedia retrieval (ICMR’23), Thessaloniki, Greece, 12–15 June 2023, pp. 172–180. New York: ACM.
21.
OtterDWMedinaJRKalitaJK.A survey of the usages of deep learning for natural language processing. IEEE Trans Neural Netw Learn Syst2020; 32(2): 604–624.
22.
LinzDGarciaRGuerraF, et al. Twitter for professional use in electrophysiology: practical guide for #EPeeps. EP Europace2021; 23(8): 1192–1199.
23.
DuttaHSChetanAJoshiB, et al. Retweet us, we will retweet you: spotting collusive retweeters involved in blackmarket services. Proceedings of the IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM’18), Barcelona, 28–31 August 2018, pp. 242–249. New York: IEEE.
24.
WuZPanSChenF, et al. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst2020; 32(1): 4–24.
25.
ArazziMNicolazzoSNoceraA, et al. The importance of the language for the evolution of online communities: an analysis based on Twitter and Reddit. Expert Syst Appl2023; 222: 119847.
26.
XueJChenJHuR, et al. Twitter discussions and emotions about the COVID-19 pandemic: machine learning approach. J Med Internet Res2020; 22(11): e20550.
27.
LiXQiuZJiangJ, et al. Conditional cross-platform user engagement prediction. ACM Trans Inf Syst2023; 42(1): 1–28.
28.
KeramatfarAAmirkhaniHBidgolyAJ.Modeling tweet dependencies with graph convolutional networks for sentiment analysis. Cogn Comput2022; 14(6): 2234–2245.
29.
AnelliVWKallooriSFerwerdaB, et al. RecSys 2021 challenge workshop: fairness-aware engagement prediction at scale on Twitter’s home timeline. In: Proceedings of the recommender systems conference (RecSys’21), Amsterdam, 27 September–1 October 2021, pp. 819–824. New York: ACM.
30.
ToramanCŞahinuçFYilmazEH, et al. Understanding social engagements: a comparative analysis of user and text features in Twitter. Soc Netw Anal Min2022; 12(1): 1–16.
31.
PiotrkowiczADimitrovaVOtterbacherJ, et al. Headlines matter: using headlines to predict the popularity of news articles on Twitter and Facebook. Proc Int AAAI Conf Weblogs Soc Media2017; 11: 656–659.
32.
AldousKKAnJJansenB.What really matters? Characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics. Behav Inf Technol2023; 42: 545–568.
33.
AldousKKAnJJansenBJ.View, like, comment, post: analyzing user engagement by topic at 4 levels across 5 social media platforms for 53 news organizations. Proc Int AAAI Conf Weblogs Soc Media2019; 3: 47–57.
34.
VolkovsMPerezFChengZ, et al. User engagement modeling with deep learning and language models. In: Proceedings of the recommender systems conference (RecSys’21), Amsterdam, 27 September–1 October 2021, pp. 22–27. New York: ACM.
35.
DanilukMDabrowskiJRychalskaB, et al. Synerise at RecSys 2021: Twitter user engagement prediction with a fast neural model. In: Proceedings of the recommender systems conference (RecSys’21), Amsterdam, 27 September–1 October 2021, pp. 15–21. New York: ACM.
36.
SanhVDebutLChaumondJ, et al. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.011082019.
37.
AbdollahiBBadamiMNutakkiG, et al. A two step ranking solution for twitter user engagement. In: Proceedings of the recommender systems challenge (RecSys’14), Foster City, CA, 6–10 October 2014, pp. 35–40. New York: ACM.
38.
PurohitHRuanYJoshiA, et al. Understanding user-community engagement by multi-faceted features: a case study on Twitter. In: Proceedings of the international workshop on social media engagement (SoME’11), Hyderabad, India, 29 March 2011.
39.
FelicioniNDonatiAConterioL, et al. Multi-objective blended ensemble for highly imbalanced sequence aware tweet engagement prediction. In: Proceedings of the recommender systems conference (RecSys’20), 22–26 September 2020, pp. 29–33. New York: ACM.
40.
KenGMengQFinleyT, et al. LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst2017; 30: 3146–3154.
41.
GodaSAgataNMatsumuraY.A stacking ensemble model for prediction of multi-type tweet engagements. In: Proceedings of the recommender systems conference (RecSys’20), Online, 22–26 September 2020, pp. 6–10. New York: ACM.
42.
MaTHuYLuY, et al. Graph neural network for customer engagement prediction on social media platforms. In: Proceedings of the Hawaii international conference on system sciences (HICSS’21), Online, Kauai, Hawaii, USA, 5 January 2021, pp. 4158–4166. ScholarSpace.
43.
KaramitsosIMohassebAKanavosA.A graph mining method for characterizing and measuring user engagement in Twitter. In: Proceedings of the international workshop on semantic and social media adaptation & personalization (SMAP’17), Bratislava, 3–4 November 2022, pp. 1–6. New York: IEEE.
44.
TangXLiuYShahN, et al. Knowing your fate: friendship, action and temporal explanations for user engagement prediction on social apps. In: Proceedings of the international conference on knowledge discovery & data mining (KDD’20), Online, 6–10 July 2020, pp. 2269–2279. New York: ACM.
45.
AlhosseiniSAMeinelC.Predicting user engagements using graph neural networks on online social networks. In: Proceedings of the international conference on high performance computing & communications (HPCC’22), Hainan, China, 18–20 December 2022, pp. 2293–2297. New York: IEEE.
46.
JinRLiuXMurataT.Predicting popularity trend in social media networks with multi-layer temporal graph neural networks. Complex Intell Syst2024; 10: 4713–4729.
47.
FanCZhouHSunY, et al. KEGNN: knowledge-enhanced graph neural networks for user engagement prediction. In: Proceedings of the ACM international conference on multimedia retrieval (ICMR’25), Chicago, IL, 30 June–3 July 2025, pp. 275–283. New York: ACM.
48.
MaTHuYLuY, et al. Customer engagement prediction on social media: a graph neural network method. Inf Syst Res2024; 36: 1887–1897.
49.
Orellana -RodriguezCGreeneDKeaneMT. Spreading the news: how can journalists gain more engagement for their tweets? In: Proceedings of the ACM conference on web science (WebSci’16), Hannover, 22–25 May 2016, pp. 107–116. New York: ACM.
50.
ZhaoQErdogduMAHeHY, et al. Seismic: a self-exciting point process model for predicting tweet popularity. In: Proceedings of the international conference on knowledge discovery and data mining (KDD’15), Sydney, NSW, Australia, 10–13 August 2015, pp. 1513–1522. New York: ACM.
51.
BaeYRyuPKimH.Predicting the lifespan and retweet times of tweets based on multiple feature analysis. ETRI J2014; 36(3): 418–428.
52.
AnelliVWDelićASottocornolaG, et al. RecSys 2020 challenge workshop: engagement prediction on Twitter’s home timeline. In: Proceedings of the recommender systems conference (RecSys’20), Online, 22–26 September 2020, pp. 623–627. New York: ACM.
53.
EpsteinZLinHPennycookG, et al. Quantifying attention via dwell time and engagement in a social media browsing environment. arXiv:2209.104642022.
54.
PetersHLiuYBarbieriF, et al. Context-aware prediction of user engagement on online social platforms. arXiv:2310.145332023.
55.
van der HarstJPAngelopoulosS. Less is more: engagement with the content of social media influencers. J Bus Res2024; 181(C): 114746.
56.
HamiltonWYingZLeskovecJ.Inductive representation learning on large graphs. Adv Neural Inf Process Syst2017; 30: 1024–1035.
57.
SkazaJBlaisB.Modeling the infectiousness of Twitter hashtags. Physica A2017; 465: 289–296.
58.
BrunsAMoonBPaulA, et al. Towards a typology of hashtag publics: a large-scale comparative study of user engagement across trending topics. Commun Res Pract2016; 2(1): 20–46.
59.
CorradiniENoceraAUrsinoD, et al. Investigating the phenomenon of NSFW posts in Reddit. Inf Sci2021; 566: 140–164.
ZhaoLSongYZhangC, et al. T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst2019; 21(9): 3848–3858.
62.
XuDRuanCKorpeogluE, et al. Inductive representation learning on temporal graphs. arXiv:2002.079622020.
63.
RossiEChamberlainBFrascaF, et al. Temporal graph networks for deep learning on dynamic graphs. arXiv:2006.106372020.
64.
HuZDongYWangK, et al. Heterogeneous graph transformer. In: Proceedings of the international World Wide Web conference (WWW’20), Taipei, 20–24 April 2020, pp. 2704–2710. New York: ACM.
65.
RiquelmeFGonzález-CantergianiP.Measuring user influence on Twitter: a survey. Inf Process Manag2016; 52(5): 949–975.
66.
NagmotiRTeredesaiADe CockM.Ranking approaches for microblog search. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, Toronto, ON, Canada, 31 August–3 September 2010, pp. 153–157. New York: IEEE.
67.
TsvetovatMKouznetsovA.Social Network Analysis for Startups: finding connections on the social web. Sebastopol, CA: O’Reilly Media, 2011.
68.
MelnikSGarcia-MolinaHRahmE.Similarity flooding: a versatile graph matching algorithm and its application to schema matching. Proceedings of the IEEE international conference on data engineering (ICDE 2002), San Jose, CA, 26 February–1 March 2002, pp. 117–128. New York: IEEE Computer Society Press.
