用于探索在线话语的实体图表
Nicholas Botzer1, Tim Weninger1
1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46656 USA.
概括
分析在线讨论显示,虽然对话最初有所分歧,但它们往往会在热门话题上趋同. 本研究介绍了一个实体图,用于绘制社交网络中的人类沟通模式.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 认知心理学 认知心理学
背景情况:
- 在线通信产生了大量适合计算分析的数字数据.
- 传统的社交网络分析模型将用户视为节点,并将概念视为它们之间的流动.
研究的目的:
- 提出一种替代的视角来分析在线话语,通过将其组织成一个静态的概念空间称为实体图.
- 通过使用这种新的框架,调查在线对话的动态及其可预测性.
主要方法:
- 从Reddit中提取和组织大规模的在线话语,将其转化为实体图.
- 量化实验来评估对话的可预测性.
- 开发一种交互式工具,在实体图上可视化对话轨迹.
- 从认知心理学中应用扩散激活函数.
主要成果:
- 在线演讲很难预测,特别是随着对话的进展.
- 谈话最初在各个主题上有很大的分歧,但倾向于走向更简单,更受欢迎的概念.
- 实体图和相关工具提供了关于沟通动态的引人注目的视觉叙述.
结论:
- 实体图框架通过绘制静态概念和动态的人类互动来理解在线通信的新方法.
- 尽管无法预测,但在线对话表现出主题分歧的普遍趋势,其次是趋同.
- 数字通信的计算分析可以提供对认知过程和社会动态的见解.
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