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Precision Ideological Education via Social Media Analytics

Lili Liu

Journal of Cases on Information Technology2026https://doi.org/10.4018/jcit.398631article
ABDC C
Weight
0.50

What the paper says

With the popularization of social media (98.7% of college students use it, with an average daily usage of 5.2+ hours), traditional ideological and political education (IPE) is facing challenges. In this study, an IPE data analysis framework integrating multi-source data (social media+campus system) and machine learning models (BERT, LDA, random forest) was constructed and verified in a key university in the east. Through stratified random sampling of 1,920 undergraduates (representing N=28,000, χ = 2.36, p=0.67), three core strategies were tested: differentiated communication, dynamic intervention, and collaborative governance. The results show that the interaction rate of students is increased by 12.4%–18.7%, the response time of questions is shortened by 64% (7.5→2.7 hours), and the accuracy of cross-departmental decision-making is improved by 51%. Ethical protection includes hierarchical informed consent, AES-256 encryption, and manual audit, and the withdrawal rate is only 1.8%.

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https://doi.org/https://doi.org/10.4018/jcit.398631

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@article{lili2026,
  title        = {{Precision Ideological Education via Social Media Analytics}},
  author       = {Lili Liu},
  journal      = {Journal of Cases on Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/jcit.398631},
}

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Precision Ideological Education via Social Media Analytics

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.