本研究报告了对主流微博客社交媒体——推特（Twitter）16天内与癌症相关话题的 语义分析。研究共收集了269万余条与癌症有关的推文（tweets），并创建了包含223 条 关键词的分类法（taxonomy）。依照推文的频率、周期、同步出现和情绪因素，分析了 超过 113万条由该分类筛选的推文并进行可视化呈现。研究结果发现：(1) 可以从推特 社交信息中检测到的、有助呈现癌症相关议题的最显见的关键词；(2) 癌症相关推文的 “每周两天” 的频率高峰；这种节奏在很大程度上受到突发新闻或新闻事件的影响； (3) 由与乳腺癌、肺癌和前列腺癌相关的推文中的关键词汇的同步呈现构成的语义网 络（semantic network），以及 (4) 表达对癌症的积极或消极情绪的情感网络（sentiment network）。同时，本文对研究的潜在理论意义和实际应用进行了讨论。

This study reports a semantic analysis of cancer-related conversation in Twitter during a 16- day period. More than 2.69 million tweets related to cancer were collected. Taxonomy consisting of 223 cancer-related key terms were created and developed. More than 1.13 million tweets filtered with the taxonomy were analyzed and visualized, in terms of the frequency, periodicity, co-occurrence and sentiments. Findings report (1) the most visible keywords, which partially illustrate the topics and message relevant to cancer, detectable from social streaming in Twitter; (2) a two-day-of-week rhythm with frequency of cancer-related tweets, which was highly influenced by breaking news or news events; (3) the key terms co-occurrence in tweets concerning breast cancer, lung cancer and prostate cancer, and (4) a sentiment network that comprises both positive and negative feelings or concerns about cancer. The potential theoretical contributions of this project and its practical implications are also discussed.