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Social Media Analytics of Smoking Cessation Intervention: User Behavior Analysis, Classification, and Prediction.

机译：戒烟干预的社交媒体分析：用户行为分析，分类和预测。

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摘要

Tobacco use causes a large number of diseases and deaths in the United States. Traditional intervention programs are based on face-to-face consulting, and social support is offered to help smoking quitters control stress and achieve better intervention outcomes. However, the scalability of these traditional intervention programs is limited by time and location. With the development of Web 2.0, many intervention programs of smoking cessation are developed online to reach a wider population. QuitNet is a popular website for smoking cessation that provides different services to help users quit smoking. It builds communities on different social media for people to discuss issues of smoking cessation and provide social support for each other. In this dissertation, we develop a comprehensive study to understand user behavior and their discussion interactions in online communities of smoking cessation. We compare user features and behaviors on different social media channels, analyze user interactions from the perspective of social support exchange, and apply data mining techniques to analyze discussion content and recommend threads for users.;Health communities are developed on different types of social media. For example, QuitNet has Web forums on its own Web site while it also has its appearance on Facebook. The user participation may vary on different social media platforms. Users may also behave differently depending on the functions and design of the social media platforms. So, as the first step in this dissertation, we carry out a preliminary study to compare smoking cessation communities on different social media channels. We analyze user characteristics and behaviors in QuitNet Forum and QuitNet Facebook with statistical analysis and social network analysis. It is found that most users of QuitNet Forum are early smoking quitters, and they participate in discussions more actively than users of QuitNet Facebook. However, users of QuitNet Facebook have a wider spectrum of quitting statuses and interaction behaviors.;Second, we are interested in user behaviors and how they exchange social support in online communities. Social support is "an exchange of resources between two individuals perceived by the provider or the recipient to be intended to enhance the well-being of the recipient". As QuitNet Forum attracts much more active users than QuitNet Facebook, it provides a better platform for our research purpose. So, we focus on QuitNet Forum, developing a classification scheme through qualitative analysis to categorize discussion topics and types of social support on the forum. Patterns of user behaviors are defined and identified. Social networks are built to analyze user interactions of social support exchange. It is found that users at different quit stages have different behaviors to exchange social support, and different types of social support flow between users at different quit stages.;Discussion topics, user behaviors and patterns of social support exchanges are thoroughly analyzed. However, due to a huge amount of information on QuitNet Forum, it is difficult for users to find proper topics or peers to discuss or interact with. It would be helpful if we could apply machine learning techniques to understand user generated information in online health communities, and recommend discussion topics to users to participate in. We develop classifiers to categorize posts and comments on QuitNet Forum in terms of user intentions and social support types. User behaviors and patterns are used to help developing various feature sets. Then, we develop recommendation techniques to recommend threads for users to participate in. Based on traditional Collaborative Filtering and content-based approaches, we integrate classification results and user quit stages to develop recommendation systems. The experiments show that integrating classification results or user health statuses can achieve the best recommendation results with different percentages of unknown data.;In this dissertation, we implement all-sided studies for online smoking cessation communities, including comprehensive analytics and applications. The proposed frameworks and approaches could be applied to other health communities. In the future, we will apply more analytics and techniques to a larger data set, and develop user-end applications to serve and improve online health intervention programs and communities.
机译：在美国，吸烟导致大量疾病和死亡。传统的干预计划基于面对面的咨询，并提供社会支持以帮助戒烟者控制压力并获得更好的干预效果。但是，这些传统干预程序的可伸缩性受到时间和位置的限制。随着Web 2.0的发展，许多在线戒烟干预计划得以开发，以覆盖更广泛的人群。 QuitNet是一个流行的戒烟网站，提供不同的服务来帮助用户戒烟。它在不同的社交媒体上建立社区，使人们可以讨论戒烟问题并相互提供社会支持。在本文中，我们开展了一项全面的研究，以了解在线戒烟社区中的用户行为及其讨论互动。我们比较不同社交媒体渠道上的用户特征和行为，从社交支持交流的角度分析用户互动，并应用数据挖掘技术分析讨论内容并为用户推荐话题。健康社区是在不同类型的社交媒体上开发的。例如，QuitNet在其自己的网站上拥有Web论坛，同时它也出现在Facebook上。用户参与可能在不同的社交媒体平台上有所不同。根据社交媒体平台的功能和设计，用户的行为也可能有所不同。因此，作为本文的第一步，我们进行了初步研究，以比较不同社交媒体渠道上的戒烟社区。我们通过统计分析和社交网络分析来分析QuitNet论坛和QuitNet Facebook中的用户特征和行为。发现QuitNet论坛的大多数用户是早期戒烟者，并且比QuitNet Facebook的用户更积极地参与讨论。但是，QuitNet Facebook的用户具有更广泛的退出状态和交互行为。其次，我们对用户行为以及他们如何在在线社区中交换社会支持感兴趣。社会支持是“提供者或接受者认为旨在增强接受者的福祉的两个人之间的资源交换”。 QuitNet论坛比QuitNet Facebook吸引了更多的活跃用户，因此它为我们的研究目的提供了一个更好的平台。因此，我们专注于QuitNet论坛，通过定性分析制定分类方案，以对论坛上的讨论主题和社会支持类型进行分类。定义和识别用户行为的模式。建立社交网络以分析社交支持交换的用户交互。结果发现，不同戒烟阶段的用户交流社交支持的行为不同，不同戒烟阶段的用户之间的社交支持流的类型也不同。深入分析了讨论主题，用户行为和社交支持交流的方式。但是，由于QuitNet论坛上的大量信息，用户很难找到合适的主题或同行进行讨论或互动。如果我们可以应用机器学习技术来了解在线健康社区中用户生成的信息，并向用户推荐讨论主题，将会很有帮助。我们开发了分类器，可以根据用户意图和社会支持对QuitNet论坛上的帖子和评论进行分类。类型。用户行为和模式用于帮助开发各种功能集。然后，我们开发推荐技术以推荐线程以供用户参与。基于传统的协作筛选和基于内容的方法，我们将分类结果和用户退出阶段进行集成以开发推荐系统。实验表明，结合分类结果或用户健康状况，可以得到不同百分比的未知数据的最佳推荐结果。本文对在线戒烟社区进行全方位的研究，包括全面的分析和应用。拟议的框架和方法可适用于其他卫生界。将来，我们将把更多的分析和技术应用于更大的数据集，并开发用户端应用程序以服务和改善在线健康干预计划和社区。

著录项

  • 作者

    Zhang, Mi.;

  • 作者单位

    Drexel University.;

  • 授予单位 Drexel University.;
  • 学科 Information science.;Web studies.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 117 p.
  • 总页数 117
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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