Verberne Suzan, Batenburg Anika, Sanders Remco, van Eenbergen Mies, Das Enny, Lambooij Mattijs S
Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands.
Centre for Language Studies, Radboud University, Nijmegen, Netherlands.
JMIR Cancer. 2019 Apr 17;5(1):e9887. doi: 10.2196/cancer.9887.
Peer-to-peer online support groups and the discussion forums in these groups can help patients by providing opportunities for increasing their empowerment. Most previous research on online empowerment and online social support uses qualitative methods or questionnaires to gain insight into the dynamics of online empowerment processes.
The overall goal of this study was to analyze the presence of the empowerment processes in the online peer-to-peer communication of people affected by cancer, using text mining techniques. Use of these relatively new methods enables us to study social processes such as empowerment on a large scale and with unsolicited data.
The sample consisted of 5534 messages in 1708 threads, written by 2071 users of a forum for cancer patients and their relatives. We labeled the posts in our sample with 2 types of labels: labels referring to empowerment processes and labels denoting psychological processes. The latter were identified using the Linguistic Inquiry and Word Count (LIWC) method. Both groups of labels were automatically assigned to posts. Automatic labeling of the empowerment processes was done by text classifiers trained on a manually labeled subsample. For the automatic labeling of the LIWC categories, we used the Dutch version of the LIWC consisting of a total of 66 word categories that are assigned to text based on occurrences of words in the text. After the automatic labeling with both types of labels, we investigated (1) the relationship between empowerment processes and the intensity of online participation, (2) the relationship between empowerment processes and the LIWC categories, and (3) the differences between patients with different types of cancer.
The precision of the automatic labeling was 85.6%, which we considered to be sufficient for automatically labeling the complete corpus and doing further analyses on the labeled data. Overall, 62.94% (3482/5532) of the messages contained a narrative, 23.83% (1318/5532) a question, and 27.49% (1521/5532) informational support. Emotional support and references to external sources were less frequent. Users with more posts more often referred to an external source and more often provided informational support and emotional support (Kendall τ>0.2; P<.001) and less often shared narratives (Kendall τ=-0.297; P<.001). A number of LIWC categories are significant predictors for the empowerment processes: words expressing assent (ok and yes) and emotional processes (expressions of feelings) are significant positive predictors for emotional support (P=.002). The differences between patients with different types of cancer are small.
Empowerment processes are associated with the intensity of online use. The relationship between linguistic analyses and empowerment processes indicates that empowerment processes can be identified from the occurrences of specific linguistic cues denoting psychological processes.
点对点在线支持小组以及这些小组中的讨论论坛可为患者提供增强权能的机会，从而帮助他们。此前大多数关于在线赋权和在线社会支持的研究都采用定性方法或问卷调查，以深入了解在线赋权过程的动态情况。
本研究的总体目标是使用文本挖掘技术分析癌症患者在线点对点交流中赋权过程的存在情况。使用这些相对较新的方法使我们能够大规模地且利用自发数据研究诸如赋权之类的社会过程。
样本包括1708个主题帖中的5534条消息，由一个癌症患者及其亲属论坛的2071名用户撰写。我们用两种类型的标签对样本中的帖子进行标注：与赋权过程相关的标签和表示心理过程的标签。后者使用语言查询与字数统计（LIWC）方法来识别。两组标签均自动分配给帖子。赋权过程的自动标注由在手动标注子样本上训练的文本分类器完成。对于LIWC类别的自动标注，我们使用荷兰语版的LIWC，它共有66个词类，根据文本中单词的出现情况分配给文本。在用两种类型的标签进行自动标注后，我们研究了：（1）赋权过程与在线参与强度之间的关系；（2）赋权过程与LIWC类别之间的关系；（3）不同类型癌症患者之间的差异。
自动标注的精度为85.6%，我们认为这足以对整个语料库进行自动标注并对标注数据进行进一步分析。总体而言，62.94%（3482/5532）的消息包含叙述内容，23.83%（1318/5532）为问题，27.49%（1521/5532）为信息支持。情感支持和提及外部来源的情况较少见。发帖较多的用户更常提及外部来源，更常提供信息支持和情感支持（肯德尔τ>0.2；P<0.001），而较少分享叙述内容（肯德尔τ=-0.297；P<0.001）。一些LIWC类别是赋权过程的重要预测指标：表示赞同（好的和是的）和情感过程（情感表达）的词是情感支持的重要正向预测指标（P=0.002）。不同类型癌症患者之间的差异较小。
赋权过程与在线使用强度相关。语言分析与赋权过程之间的关系表明，可以从表示心理过程的特定语言线索的出现情况中识别赋权过程。
