Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network.
Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network.
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DOI：
10.1109/tcss.2016.2591880
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发表时间：
2016-06
影响因子：
5
通讯作者：
Honavar V
中科院分区：
文献类型：
--
作者：
Bui N;Yen J;Honavar V
在线卫生社区为患者提供了有用的信息来源和社会支持。美国癌症协会的癌症幸存者网络（CSN）是一个拥有173,000名成员的社区，是癌症患者、幸存者和护理人员最大的在线网络。CSN的讨论主题通常是由寻求CSN其他成员支持的癌症幸存者发起的。讨论线程是多方对话，通常提供社会支持的来源，例如，通过使线程发起者的情绪从消极转变为积极。虽然先前关于癌症幸存者的研究表明，在线健康社区的成员从参与此类社区中获益，但缺乏对促成所观察到的益处的因素的因果解释。我们引入了一个新的框架来研究CSN中情绪动态的时间因果关系。我们构建了一个概率计算树逻辑表示和相应的概率Kripke结构来表示和推理线程中帖子情绪随时间的变化。我们使用机器学习训练的情感分类器，对一组手动标记有情感标签的帖子进行分类，以表达积极或消极的情绪。我们分析了概率Kripke结构，以确定CSN论坛中帖子发起者情绪变化的初步原因及其意义。我们发现，回复的情绪似乎对线程发起者的情绪有因果关系。我们的实验还表明，结论在情感分类器的分类阈值的选择方面是鲁棒的；（ii）以及所使用的具体情感分类器的选择。我们还扩展了时间因果分析的基本框架，以纳入概率Kripke结构状态中的不确定性，这是由于使用了不完美状态换能器（在我们的例子中是情感分类器）。我们对CSN情感动态的时间因果关系分析为CSN网络社区的设计者、管理者和版主提供了新的见解，以促进和加强互动，从而更好地满足CSN参与者的社会支持需求。所提出的分析时间因果关系的方法在各种环境中具有广泛的适用性，在这些环境中，底层系统的动态可以根据响应内部或外部输入而变化的状态变量进行建模。
Online health communities constitute a useful source of information and social support for patients. American Cancer Society’s Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multi-party conversations that often provide a source of social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of an online health community derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a Probabilistic Computation Tree Logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the (i) classification threshold of the sentiment classifier; (ii) and the choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs.