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Machine Learning and Data Mining for User Classification, 2nd Edition

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Special Issue Editors

Special Issue Information

Dear Colleagues,

We are pleased to present the 2nd Edition of the Special Issue, titled, “Machine Learning and Data Mining for User Classification” (https://www.mdpi.com/journal/information/special_issues/9ZUZP40XA7).

Modern internet is characterized by its many users, the multitude of web services offered, and the increased complexity in accessing digital resources. In this context, more sophisticated threats have increasingly occurred, requiring new methods for protecting and facilitating users. Furthermore, the large amount of stored raw data, which is growing at a dizzying pace daily, hides information that is not immediately available and requires time and effort.

This Special Issue suggests new approaches for creating user profiles to protect unsuspecting users, enhancing user authentication, and extracting information from textual data. Moreover, this Special Issue is interested in document classification and, generally, for methods protecting Internet users, making better use of the services offered, and extracting the available information using data derived mainly from text and typing.

Dr. Ioannis Tsimperidis
Dr. Eleni Vrochidou
Prof. Dr. George A. Papakostas
Guest Editors

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Keywords

data mining
information retrieval
text analysis
data clustering
user authentication
user profiling
user classification by inherent and acquired characteristics
natural language processing
author classification
keystroke dynamics
typing pattern recognition
content classification
digital text forensics
typing behavior

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Research

18 pages, 1446 KB

Open AccessArticle

Unveiling the Impact of Mandatory IP Location Disclosure on Social Media Users’ Shared Emotions: A Regression Discontinuity Analysis Based on Weibo Data

by Heng Zhang, Aiping Gao, Zhuyu Chen and Xinyuan Lu

Information 2026, 17(1), 63; https://doi.org/10.3390/info17010063 - 9 Jan 2026

Viewed by 218

Abstract

Social media serves as a vital channel for emotional expression, yet mandatory IP location disclosure raises concerns about how reducing anonymity affects users’ shared emotions, particularly in privacy-sensitive contexts such as mental health discussions. In 2022, all Chinese social media platforms implemented this disclosure feature. This study examines the emotional and behavioral consequences of Sina Weibo’s mandatory IP location disclosure policy, which took effect on 28 April 2022. We collected 193,761 Weibo posts published under the topic of depression from 1 March to 30 June 2022, and applied sentiment analysis combined with regression discontinuity in time (RDiT) to estimate causal effects around the policy threshold. Results indicate that the policy significantly intensified negative emotional expression: the estimated discontinuity is −1.3506 (p < 0.01), meaning posts became more negative immediately after implementation. In contrast, the effect on positive sentiment was comparatively weak and mostly statistically insignificant. Behavioral changes were also observed: both average daily posting volume and average text length are declined. These findings demonstrate that mandatory disclosure can suppress self-disclosure and amplify negative emotional tone in privacy-sensitive settings, offering practical guidance for users, platform designers, and policymakers on implementing transparency features responsibly. Full article

(This article belongs to the Special Issue Machine Learning and Data Mining for User Classification, 2nd Edition)

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