1 views collected around this technical thread.
This article describes a Python‑based approach that identifies and eliminates WeChat group members who repeatedly post advertisements by defining heuristic rules, capturing screen coordinates with the pynput library, simulating mouse‑clicks and keyboard shortcuts, and processing the extracted data to flag suspicious accounts.
This article introduces the Huajiao platform's text anti‑spam architecture, analyzes spam categories and challenges, compares rule‑based and machine‑learning approaches, details traditional NLP methods and the TextCNN deep‑learning model, provides its TensorFlow implementation, and describes the online deployment workflow.
This article chronicles the three‑generation evolution of Zhihu’s anti‑cheat platform Wukong, detailing its business context, spam taxonomy, multi‑layered control methods, architectural redesigns, strategy language improvements, graph‑based risk analysis, and the continuous integration of big‑data and machine‑learning techniques to combat content and behavior spam.
The discussion outlines practical anti‑spam strategies—including text length limits, keyword replacement, trie‑based data structures, AC automata, Bayesian and vector‑similarity algorithms, and PHP extensions such as libdatrie—while also sharing performance metrics and resource links for implementing robust content filtering systems.
This article describes how Zhihu combats large‑scale spam by applying text and behavior clustering techniques—using Jaccard similarity, SimHash fingerprinting, and Spark‑based graph partitioning—to efficiently identify and group similar spammy content and actions.
This article provides a comprehensive overview of search engine evolution, core technologies such as crawling, indexing, retrieval and link analysis, platform foundations including cloud storage and computing, and techniques for improving search results through anti‑spam, user‑intent analysis, deduplication and caching.
This article introduces a series of algorithms for analyzing the Weibo social network, including label propagation, LDA‑based user similarity, time‑aware and interaction‑aware similarity measures, community detection, influence ranking via PageRank variants, and methods for identifying spam users, illustrating how these techniques can be applied to large‑scale social media data.
The article presents a comprehensive overview of algorithms for analyzing Weibo’s social network, covering label propagation, user similarity via LDA, temporal and interaction factors, community detection, influence ranking using PageRank variants, and methods for identifying spam accounts.