Expertise-aware news feed updates recommendation: a random forest approach


With social media being widely used around the world, and because of the large amount of data, users are overcome by updates displayed chronologically in their news feed. Furthermore, most updates are considered irrelevant. To help beneficiary users quickly catch up with the relevant content, ranking news feed updates in descending relevance order has been achieved based on the prediction of a relevance score between a beneficiary and a new update in the news feed. Four types of features are generally used to predict the relevance: (1) the relevance of the update content to the beneficiary’s interests; (2) the social tie strength between the beneficiary and the update’s author; (3) the author’s authority; and (4) the update quality. In this work, from the biography and the textual content posted, we propose an approach that infers and uses another type of feature which is the expertise of the update’s author for the corresponding topic. Following extensive experiments on a real dataset crawled from Twitter, the results show that infer the author’s expertise is critical for identifying relevant updates in news feeds.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6


  1. 1.

  2. 2.

  3. 3.

    A project that extracts structured content from Wikipedia.

  4. 4.

  5. 5.

    A string of characters used to identify a resource.

  6. 6.

    A method that use multiple machine learning algorithms to obtain better predictive performance that could be obtained from any of the constituent learning algorithms alone.

  7. 7.


  1. 1.

    Jin, S., Lin, W., Yin, H., Yang, S., Li, A., Deng, B.: Community structure mining in big data social media networks with MapReduce. Clust. Comput. 18(3), 999–1010 (2015)

    Article  Google Scholar 

  2. 2.

    Xu, Z., Liu, Y., Yen, N., Mei, L., Luo, X., Wei, X., Hu, C.: Crowdsourcing based description of Urban emergency events using social media big data. In: IEEE Transactions on Cloud Computing, p. 1 (2016)

  3. 3.

    Srividya, M., Irfan Ahmed, M.S.: A filtering of message in online social network using hybrid classifier. Clust. Comput. (2017).

    Article  Google Scholar 

  4. 4.

    Arularasan, A.N., Suresh, A., Seerangan, K.: Identification and classification of best spreader in the domain of interest over the social networks. Clust. Comput. 22, 4035–4045 (2018)

    Article  Google Scholar 

  5. 5.

    Bontcheva, K., Gorrell, G., Wessels, B.: Social media and information overload: survey results. arXiv preprint arXiv:1306.0813 (2013)

  6. 6.

    Vougioukas, M., Androutsopoulos, I., Paliouras, G.: Identifying Retweetable Tweets with a Personalized Global Classifier. CoRR. abs/1709.06518 (2017)

  7. 7.

    Ramage, D., Dumais, S.T., Liebling, D.J.: Characterizing microblogs with topic models. ICWSM 10(1), 16 (2010)

    Google Scholar 

  8. 8.

    Kuang, L., Tang, X., Yu, M., Huang, Y., Guo, K.: A comprehensive ranking model for tweets big data in online social network. EURASIP J. Wirel. Commun. Netw. 2016(1), 46 (2016)

    Article  Google Scholar 

  9. 9.

    Agarwal, D., Zhang, L., Chen, B.-C., He, Q., Hua, Z., Lebanon, G., Ma, Y., Shivaswamy, P., Tseng, H.-P., Yang, J.: Personalizing LinkedIn Feed, pp. 1651–1660. ACM Press, New York (2015)

    Google Scholar 

  10. 10.

    Belkacem, S., Boukhalfa, K., Boussaid, O.: News feeds triage on social networks: a survey. In: Proceedings of The 2nd International Conference on Computing Systems and Applications (CSA), pp. 34–43 (2016)

  11. 11.

    Alp, Z.Z., Öğüdücü, G.: Identifying topical influencers on twitter based on user behavior and network topology. Knowl. Based Syst. 141, 211–221 (2018)

    Article  Google Scholar 

  12. 12.

    Pal, A., Herdagdelen, A., Chatterji, A., Taank, S., Chakrabarti, D.: Discovery of Topical Authorities in Instagram, pp. 1203–1213. ACM Press, New York (2016)

    Google Scholar 

  13. 13.

    Wagner, C., Liao, V., Pirolli, P., Nelson, L., Strohmaier, M.: It’s Not in their Tweets: Modeling Topical Expertise of Twitter Users, pp. 91–100. IEEE, Washington (2012)

    Google Scholar 

  14. 14.

    Yu, X., Dong, Z., Lawless, S.: Inferring Your Expertise from Twitter: Combining Multiple Types of User Activity, pp. 589–598. ACM Press, New York (2017)

    Google Scholar 

  15. 15.

    Wei, W., Cong, G., Miao, C., Zhu, F., Li, G.: Learning to find topic experts in Twitter via different relations. IEEE Trans. Knowl. Data Eng. 28(7), 1764–1778 (2016)

    Article  Google Scholar 

  16. 16.

    Li, X., Cheng, S., Chen, W., Jiang, F.: Novel user influence measurement based on user interaction in microblog. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 615–619. IEEE (2013)

  17. 17.

    Belkacem, S., Boukhalfa, K., Boussaid, O.: Leveraging expertise in news feeds: a Twitter case study. In: Journées francophones sur les Entrepôts de Données et l’Analyse en ligne (EDA), vol. 14, pp. 1–16. Revue des Nouvelles Technologies de l’Information (2018)

  18. 18.

    Berkovsky, S., Freyne, J.: Personalised network activity feeds: finding needles in the haystacks. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds.) Mining. Modeling, and Recommending ’Things’ in Social Media, vol. 8940, pp. 21–34. Springer, Cham (2015)

    Google Scholar 

  19. 19.

    Gligoric, K., Anderson, A., West, R.: How Constraints Affect Content: The Case of Twitter’s Switch from 140 to 280 Characters. arXiv preprint arXiv:1804.02318 (2018)

  20. 20.

    Nguyen, T.-T., Nguyen, T.-T., Ha, Q.-T.: Applying Hidden Topics in Ranking Social Update Streams on Twitter, pp. 180–185. IEEE, Washington (2013)

    Google Scholar 

  21. 21.

    Shen, K., Jianmin, W., Zhang, Y., Han, Y., Yang, X., Song, L., Xiao, G.: Reorder user’s tweets. ACM Trans. Intell. Syst. Technol. 4(1), 1–17 (2013)

    Article  Google Scholar 

  22. 22.

    De Maio, C., Fenza, G., Gallo, M., Loia, V., Parente, M.: Time-aware adaptive tweets ranking through deep learning. Future Gen. Comput. Syst. (2017).

    Article  Google Scholar 

  23. 23.

    Feng, W., Wang, J.: Retweet or Not?: Personalized Tweet Re-ranking, p. 577. ACM Press, New York (2013)

    Google Scholar 

  24. 24.

    Belkacem, S., Boukhalfa, K., Boussaid, O.: Tri des actualités sociales: Etat de l’art et Pistes de recherche. In: Journées francophones sur les Entrepôts de Données et l’Analyse en ligne (EDA), vol. 13, pp. 85–100. Revue des Nouvelles Technologies de l’Information (2017)

  25. 25.

    Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative Personalized Tweet Recommendation, p. 661. ACM Press, New York (2012)

    Google Scholar 

  26. 26.

    Zheng, Z., Chen, K., Sun, G., Zha, H.: A Regression Framework for Learning Ranking Functions Using Relative Relevance Judgments, p. 287. ACM Press, New York (2007)

    Google Scholar 

  27. 27.

    Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2012)

    Google Scholar 

  28. 28.

    Nagmoti, R., Teredesai, A., De Cock, M.: Ranking Approaches for Microblog Search, pp. 153–157. IEEE, Washington (2010)

    Google Scholar 

  29. 29.

    Martín-Vicente, M.I., Gil-Solla, A., Ramos-Cabrer, M., Blanco-Fernàndez, Y., López-Nores, M.: Semantic inference of user’s reputation and expertise to improve collaborative recommendations. Expert Syst. Appl. 39(9), 8248–8258 (2012)

    Article  Google Scholar 

  30. 30.

    Liao, Q.V., Wagner, C., Pirolli, P., Fu, W.-T.: Understanding Experts’ and Novices’ Expertise Judgment of Twitter Users, p. 2461. ACM Press, New York (2012)

    Google Scholar 

  31. 31.

    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  32. 32.

    Kapanipathi, P., Jain, P., Venkataramani, C., Sheth, A.: User interests identification on Twitter using a hierarchical knowledge base. In: Hutchison, D., Kanade, T., Kittler, J., Kleinberg, J.M., Kobsa, A., Mattern, F., Mitchell, J.C., Naor, M., Nierstrasz, O., Pandu Rangan, C., Steffen, B., Terzopoulos, D., Tygar, D., Weikum, G., Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) The Semantic Web: Trends and Challenges, vol. 8465, pp. 99–113. Springer, Cham (2014)

    Google Scholar 

  33. 33.

    Xu, Y., Zhou, D., Lawless, S.: Inferring Your Expertise from Twitter: Integrating Sentiment and Topic Relatedness, pp. 121–128. IEEE, Washington (2016)

    Google Scholar 

  34. 34.

    Pan, Y., Cong, F., Chen, K., Yu, Y.: Diffusion-Aware Personalized Social Update Recommendation, pp. 69–76. ACM Press, New York (2013)

    Google Scholar 

  35. 35.

    Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.-Y.: An empirical study on learning to rank of tweets. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 295–303. Association for Computational Linguistics (2010)

  36. 36.

    Uysal, I., Croft, W.B.: User Oriented Tweet Ranking: A Filtering Approach to Microblogs, p. 2261. ACM Press, New York (2011)

    Google Scholar 

  37. 37.

    Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)

    Google Scholar 

  38. 38.

    Biau, G., Scornet, E.: A random forest guided tour. TEST 25(2), 197–227 (2016)

    MathSciNet  Article  Google Scholar 

  39. 39.

    Breiman, L., Cutler, A.: Random forests-classification description: random forests. (2007). Accessed 20 Sept 2017

  40. 40.

    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)

    Article  Google Scholar 

  41. 41.

    Malekipirbazari, M., Aksakalli, V.: Risk assessment in social lending via random forests. Expert Syst. Appl. 42(10), 4621–4631 (2015)

    Article  Google Scholar 

  42. 42.

    Khan, R., Hanbury, A., Stoettinger, J.: Skin Detection: A Random Forest Approach, pp. 4613–4616. IEEE, Washington (2010)

    Google Scholar 

  43. 43.

    Färber, M., Ell, B., Menne, C., Rettinger, A.: A comparative survey of dbpedia, freebase, opencyc, wikidata, and yago. Semant. Web J. 1, 1–5 (2015)

    Google Scholar 

  44. 44.

    Sammut, C., Webb, G.I.: Encyclopedia of Machine Learning. Springer, Berlin (2011)

    Google Scholar 

  45. 45.

    Ester, M.: Recommendation in social networks. In: RecSys, pp. 491–492 (2013)

Download references

Author information



Corresponding author

Correspondence to Sami Belkacem.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Belkacem, S., Boukhalfa, K. & Boussaid, O. Expertise-aware news feed updates recommendation: a random forest approach. Cluster Comput 23, 2375–2388 (2020).

Download citation


  • Social media
  • News feed updates
  • Relevance
  • Ranking
  • Expertise
  • Twitter