抽象
目的
本文的目的是探讨如何在电子政务中使用社交媒体来加强政府与公众之间的互动。
设计/方法/方法
本研究将交互性的决定因素分为深度和广度两个方面，即结构特征和内容特征，采用一般线性模型和方差分析方法分析了 14,910 篇帖子，这些帖子属于中国最大的社交媒体平台之一新浪 96 个最受欢迎的政府账户的榜首。
发现
研究的主要发现是，多媒体元素比例和外部链接比例这两个变量都对交互性的广度有积极影响，而多媒体特征的比例和原创性比例对交互性的深度有显著影响。
原创性/价值
贡献如下。首先，作者分析了政府帖子的属性和主题，以描绘出地方政府如何使用微博作为与公众互动的沟通渠道的丰富画面。其次，作者从广度和深度的角度概念化了政府在线交互性。第三，作者从两个方面确定了增强交互性的因素：结构特征和内容特征。最后，作者就如何加强社交媒体中的电子政务互动向地方政府提出了建议。
关键字
引文
Hao， x.， Zheng， D.， Zeng， Q. 和 Fan， W. （2016），“如何加强电子政务的社交媒体互动性：来自中国的证据”， 在线信息评论 ，第 40 卷第 1 期，第 79-96 页。https://doi.org/10.1108/OIR-03-2015-0084
出版商
：翡翠集团出版有限公司
版权所有 © 2016， Emerald Group Publishing Limited
1. 引言
社交媒体包括 Facebook、Twitter、博客、wiki 和 YouTube 等社交网络应用程序，是指一组支持社交互动的在线工具。社交媒体被认为是 Web 2.0 革命的一部分，其特点是用户生成的内容、在线互动和社交环境中的内容共享（Livingstone，2008 年;Merchant，2012 年）。社交媒体为商业组织和个人之间的沟通和信息共享带来了重大而普遍的变化（Kietzmann et al.， 2011）。对于个人来说，参与社交媒体的好处已经超越了简单的社交分享，还在于建立声誉、带来职业机会和金钱收入（Tang et al.， 2012）。对于商业实体来说，参与社交媒体的好处可以使沟通变得容易，并为管理人员带来向在线社区成员推销其产品和/或服务的机会。社交媒体甚至促进了客户价值共创，表示在新产品或服务开发的背景下，生产者和客户之间积极、创造性和社交的协作过程（Piller 等人 ，2012 年;Gnyawali et al.， 2010）。
使用社交媒体的好处不仅限于个人和私营部门组织。政府也可以受益。政府中的社交媒体正在成为全球电子政务 （e-government） 研究和实践的主要趋势之一（Bertot et al.， 2012a）。它有望通过促进各种内部和外部利益相关者之间的沟通和协调来提高公共部门的有效性和合法性（Meijer 和 Thaens，2013 年），这也帮助现任政府履行其开放政府倡议，以提高透明度、参与度和协作（Lee 和 Kwak，2012 年;Mergel，2013a，b）。对于政府来说，社交媒体提供了以创新方式将公民的信息和意见纳入政策制定的机会，通过在社交媒体渠道上分享信息来提高透明度，并与公众合作为政府问题做出决策或解决方案（Mergel，2013a，b）。Bertot 等人 （2010a） 认为，政府对社交媒体的使用提供了几个关键机会，即民主参与和参与、联合生产以及众包解决方案和创新。因此，可以通过社交媒体互动获得许多机会。
尽管政府越来越关注使用社交媒体，因为他们更加依赖社交媒体与公众互动，但缺乏实证证据来证明社交媒体在改善政府与公众之间的互动方面的商业价值。事实上，社交媒体的潜在价值并未得到充分展示，公民对基于社交媒体的电子政务服务的接受度仍然是一个问题（Susanto 和 Goodwin，2013 年）。尽管现在许多电子政务活动都集中在社交媒体上，但政府和公众之间仍然缺乏有效的对话和反馈机制。从技术上讲，政府和公众之间的交流得到了充分的支持，但在实践中，社交媒体的使用通常是单向的，而不是互动的（Kuzma，2010）。然而，由于缺乏电子政务的有形目标、文化、控制理念和资源管理，许多机构不愿意衡量他们的在线互动，甚至由于现有的法律法规而被阻止（Zhao，2011 年;Magro，2012 年）。因此，政府如何积极让公众参与以获得公民的观点和专业知识仍然是一个巨大的挑战。此外，政府官员仍然缺乏关于如何更有效地使用社交媒体的指导（Mergel，2013a，b）。最近，一些研究从理论角度关注双向公众参与。 Ferro 等人 （2013 年） 从创新扩散理论的角度分析了政府机构集中使用多个社交媒体的一种高级形式，这些理论已经开始从利用这些强大的双向通信渠道的简单形式转向更复杂和复杂的形式。他们推断这种方法具有广泛传播的基本前提。Mergel （2013a， b） 提出了一个框架来追踪任务支持的在线互动以及由此产生的社交媒体策略。显然，需要更多的理论和实证研究来测试和解释社交媒体如何改善政府与公众之间互动的机制。
在这项研究中，我们努力寻找政府和公民之间成功沟通和互动的决定因素。解决此问题的方法有两个步骤。首先，如何衡量政府和公民在社交媒体上的互动性？其次，哪些因素会显著影响政府与公众的互动，以及政府如何充分利用社交媒体来促进与公众的互动？
为了回答这些问题，我们从中国 96 个流行的政府微博账户中收集并分析了样本数据。然后，我们通过分析政府帖子的属性（例如是否包含图片、视频和 URL）和内容来概念化交互行为。然后，我们确定可以促进交互性的因素。最后，考虑到这些发现，我们向政府提出了如何利用社交媒体来增强政府和公民互动的建议。
论文的其余部分组织如下：第 2 节提供了与先前关于政府使用社交媒体的研究相关的背景信息，并确定了研究差距。第 3 节提供了我们的研究模型、数据准备和从社交媒体中提取。第 4 节介绍了分析结果及其相关讨论和对政府的影响。第 5 节总结了本文并为未来的研究提供了方向。
2. 背景和先前的文献
2.1. 社交媒体在电子政务中的应用
在过去的几年里，政府行政部门的机构和部门已经开始使用许多社交媒体应用程序。这些应用程序有助于提高电子参与和参与度，这也是大多数电子政务社交媒体项目的最初主要目标（Bertot 等人 ，2010b;Criado et al.， 2013）。
社交媒体为公众提供了更具互动性和更广泛的功能，以理解电子政务（Golbeck et al.， 2010）。社交媒体旨在创新政府的内部运作方式以及它们与政府外部公众的互动方式（Criado et al.， 2013）。政府社交媒体应用的一个典型例子是美国，奥巴马总统在担任总统候选人时成为使用社交媒体的坚定倡导者。
在社交媒体环境中，政府机构不仅扮演着信息/服务提供商的角色，还需要制定策略来处理公众的在线行为，快速响应公共信息查询，以及从用户生成的内容中发现有价值的信息（Chun et al.， 2010）。社交媒体作为政府机构与公众之间电子参与和有效双向沟通的良好平台，用于提交和讨论公共问题，甚至充当政府与公众之间的对话渠道（Kassen，2013）。政府社交媒体应用的开放政府成熟度模型旨在指导政府机构评估其当前的开放政府成熟度水平，并以系统和渐进的方式迈向更高的成熟度水平。另一方面，较高的成熟度级别面临更多的技术和管理复杂性以及更大的挑战和风险（Lee 和 Kwak，2012 年）。
2.2. 政府使用社交媒体的互动性
社交媒体应用程序的主要类型包括博客、wiki、社交网络和媒体共享、微博和社交媒体混搭（Bertot et al. 2012b），这是一种特殊类型的混搭应用程序，它依赖于各种开放 API 和提要来组合来自不同社交媒体网站的公开可用内容（He 和 Zha，2014).社交媒体为公众提供更多互动功能，让他们了解电子政府。为了促进电子政务的互动性，甚至提高透明度、参与度和合作性，政府使用社交媒体与公民进行交流通常有三种互动——信息共享、交易和合作（McNutt 和 Brainard，2010 年;Mergel，2013a，b）。
交互性的概念至少可以追溯到 40 年前，属于控制论和自动化研究等领域。在当代传播研究中，互动性本身通常不被视为值得研究的现象，而是作为交流的同义词，带有一些期望（Huhtamo，1998）。Rogers 是最早提供适用于在线上下文的交互性定义的公司之一，他将其描述为一种与用户对话的新通信系统，通常以计算机作为该过程的组成部分之一。他解释说，交互性是一个变量，并且是一种给定的通信技术，可以或多或少地具有交互性（例如，实时聊天与网站内容）（Rogers，1986 年）。
交互性的概念与电子政务密切相关。Gartner Group Inc. 开发了一个关于电子政务四个阶段的模型：存在、交互、交易和转型 （Baum 和 Maio，2000 年）。在这四个阶段中，彼此之间会发生某种程度的交互。在电子政务的第一阶段，存在意味着通过将用户链接到官方文档来交互。然而，在转型阶段，互动的复杂性发生了变化，公民可以在线投票。其他人则描述了基于任务的五阶段电子政务模型。互动的特征会随着政府各个阶段的移动而变化（Baum 和 Maio，2000 年;Coursey 和 Norris，2008 年）。Van Dijk 承认并解释了交互性的各种特征 （Van Dijk， 2006）。他确定了交互性的四个层次——人类之间、人类与中介机器之间、人类之间通过媒体的交互性，甚至媒体之间或机器之间的交互性。交互性水平可以通过双向通信、高度同步、对交互的控制以及对上下文和含义的理解来实现（Van Dijk，2006）。Suen 提出，电子政务的功能，尤其是社交媒体互动，可以从三个不同的交互层次上观察到：单向交互、半双向交互和双向交互（Suen， 2006）。单向交互侧重于以基于电子邮件的订阅、政府网站上的信息访问、在线投票、民意调查或调查的形式提供信息、教育或政府信息。 半双向交互包括在线论坛、在线提交评论以及在线信息或服务请求。最后，双向交互参与包括网页个性化、网站信息搜索、信息查询、GIS 地图以及虚拟城市游览。
根据上面显示的文献综述，我们知道交互性的概念被用来衡量政府和公民之间的沟通效率和有效性。政府与公民之间的交互性可以分为两种类型，即单向交互性（communication）和双向交互性（communication）。如今，大多数电子政务计划通常侧重于单向交互，这意味着仅向公民和客户提供信息。世界各地的政府都缺乏社交媒体应用程序的双向交互性。虽然社交媒体促进了政府与公民的互动，甚至最终推动了创新，但关于政府使用社交媒体的互动性的研究是有限的，并且散布在许多不同的领域（Welch 和 Fulla，2005 年;Suen，2006 年）。
2.3. 促进政府信息交互的措施
在新媒体技术的背景下，互动通信主要是指通信系统双方之间的互换能力和影响程度：信息发送者和接收者（Walther et al.， 2005）。成功的信息交互需要信息用户和信息生成者双方的努力。因此，政府与公众之间的信息互动，既包括政府发布的信息和公众的反馈/评论，也包括政府的回应。沿着这条思路，Sobkowicz 和 Kaschesky 使用意识和参与来评估社交媒体的影响（Sobkowicz et al.， 2012）。截至目前，现有文献主要关注政府信息披露的影响是政府自身，而不是普通公众。
研究发现，通过其持续运营成功实施社交媒体战略可以增加公民对政府的信任（Park 和 Cho，2009 年）。目前，社交媒体互动策略主要集中在两个方面;一个是计算机介导的人际互动，另一个是与社交媒体本身的互动（Stromer-Galley，2000）。计算机介导的人际互动涉及评论和点赞的数量，而参与将社交媒体作为交互式多媒体，允许在社交媒体中集成各种形式的内容，例如文本、图像和视频，以吸引眼球。研究发现，主题、短链接、提及和短链接的比例的使用对用户的转发行为产生了非常显着的影响（Petrovic 等人 ，2011 年;Liu et al.， 2013）。
根据上述文献，随着电子政务计划的发展和更多机构遵循使用社交媒体的授权，很明显，成功实施需要采取一系列行动来促进政府与公众之间的互动。政府机构需要考虑交互性的含义以及如何衡量它。他们需要考虑帖子的主题和内容。他们需要根据帖子的主题和内容中提取的功能，采取适当的措施，以获得公众的用户评论，提高运营效率和服务质量。然而，现有的文献很少关注政府使用社交媒体的互动性测量。
3. 研究方法
在本文中，提出了一个有用的观点，即交互性，用于研究地方政府对社交媒体的使用情况。社交媒体在提高政府的可信度、透明度和参与度方面发挥着重要作用。接下来，我们将详细介绍如何概念化交互性并提出基于交互性的分析框架。
3.1. 分析框架
政府中的交互性描述了政府与公众之间的沟通程度。良好的交互性使公众能够更多地参与在线互动（提供评论和接收反馈），并鼓励公众参与（而不是成为旁观者）。尽管交互性的概念有许多特征、影响和描述（Downes 和 McMillan，2000），但它本质上是信息通信：例如，政府通过社交媒体向公众发布信息，然后公众通过评论或点赞做出回应或提供反馈。因此，政府与公众之间的交互核心可以用来捕捉这种传输现象，就像通信领域中单工通信和双工通信的基本概念一样（ 维基百科，2008 年）。政府向公众传输的信息，只是单工通信，可以用传输深度来衡量，而公众向政府提供的信息，是双工通信，可以用传输广度来衡量。广度评估了谁通过社交媒体与政府互动，显示了政府与公众互动的数量和频率。深度评估公众通过社交媒体与政府互动的参与程度，显示了政府与公众之间的沟通质量。
考虑到政府社交媒体应用的具体情况，有三个常见项目可以衡量互动程度——转发、评论和点赞政府帖子。广度的概念是互动性的子维度之一，由转发的数量来衡量，它显示了有多少人通过他们的在线社交网络转发或转发帖子。同时，交互子维度深度的另一个概念，以点赞和评论的数量来衡量，显示了有多少人提供反馈或回应政府帖子。本研究的整体分析框架如图 1 所示。
为了回答哪些因素将增强政府和公民之间社交媒体互动的问题，我们将与社交媒体互动相关的特征分为两类：内容特征和结构特征。这种分类是我们解释交互性的研究框架的基础。这意味着应考虑两种类型的特征，即帖子的内容和结构（Jonassen et al.， 1993;Zhang et al.， 2014;Liu et al.， 2015）。描述社交媒体客观特征的结构特征，例如特殊字符、多媒体元素，是基于帖子格式的属性，没有使用内容中包含的任何信息，而专注于社交媒体内容属性的内容特征，例如主观意见、情绪（积极或消极），是帖子的主题和帖子中论点质量的语义表现（Balahur 和 Steinberger， 2009 年;Zhang et al.， 2014）。结构特征和内容特征在社交媒体的普及中都起着关键作用（Zhang et al.， 2014;Liu et al.， 2015）。 表 I 显示了这两个类别中的特征列表。
3.2. 数据准备
新浪微博是中国最著名的社交媒体平台之一，为商业组织、个人和行政组织提供服务。中国的大多数行政组织都在新浪微博上建立他们的账户。截至 2014 年底，新浪微博识别出 94,164 个官方组织账户。我们选择新浪微博作为数据源，因为它很受欢迎且无处不在。我们收集了“新浪微博排行榜前 100 名”上列出的帐户中的帖子。
我们在数据收集过程中使用以下标准。首先，我们关注那些在新浪微博最具影响力微博中排名前 100 的微博账户。其次，我们从中收集数据的账户必须在过去四天内至少发布一篇博文。第三，每个微博账号在上周至少要有一条评论或一条转发。
根据上述三条规则，初步检测出 100 个政府微博账号，并收集了这些账号的帖子和相关信息。在筛选出不符合上述条件的账户后，我们收集了 2013 年 11 月 11 日至 11 月 15 日发布的 96 个政府微博账户的所有帖子。每条记录包括博主姓名、点赞数、转发数、评论数、发布时间以及多媒体功能，包括图片、视频、外链、提及（@）、话题标签（#）、情感等。
3.3. 通过内容分析提取内容特征
3.3.1. 内容特征的编码方案
根据以往文献，政府社交媒体的特点包括政府职能的体现、公共政策制定的讨论、促进政府信息的宣传以及鼓励大众政治参与（Liang，2012）。为了强调政府社交媒体帖子的上述四个核心特征，我们确定了四个主题，即政府事务、公共服务、新闻报道和政策交流。
政府事务是关于国家发展问题的讨论，如全国人民代表大会和中国人民政治协商会议的重大报告及其重要会议、地方发展活动、市政建设和工程项目，以及政治领导人的任、免、谈话。公共服务主题包括与民生问题相关的各种服务，例如交通安全、食品安全、住房、旅行、就业、协会发展。新闻报道的主题包括发布与民生有关的常规新闻或紧急新闻。最后，政策交流是关于引入行政工作。
然后，编码方案旨在提取定性信息进行定量分析，以构建适当的主题类别，并将每个帖子分类在这些主题下（Krippendorff，2004）。Franzosi （1989） 使用了一种基于语义定义的类别的更强大、更灵活的编码工具。Roberts （2000） 提出了一个定量文本分析的概念框架，其中变量与主题、语义或网络相关。Fan 和 Gordon （2014） 提出了社交媒体分析的三个阶段过程，其中包括：文本捕获、理解和演示。按照他们的想法，我们确定了政府发布的初始帖子的属性和主题，然后我们根据表 II 中呈现的属性开发了一个编码方案。
编码过程包括两个步骤。首先，我们使用解析工具 Institute of Computing Technology Chinese 词汇分析系统 （ICTCLAS） 从每个帖子中提取特征词。这些特征词将帮助编码人员对帖子分类进行推断。其次，制作了手动标签。每个编码员都会阅读帖子，参考 ICTCLAS 中的特征词，并将帖子分类为特定类别。由于编码任务涉及人工编码人员，因此由于手动分析过于灵活，标记可能变得无法复制。因此，特征词列表对于为内容分析过程提供稳定性是必要的。对于手动内容编码，聘请了 8 名训练有素的研究生分别对帖子进行编码。学生们被分为四组，每组分别处理 24 个账户的帖子。每组中有两名学生独立进行分类。他们将所有帖子分为两个内容类别，与政府事务相关和无关，分别编码 1 和 0。如果帖子与政府事务有关，它将进一步分为以下四个子类，政府事务，编码为“A”;公共服务，编码为“B”;新闻报道，编码为“C”;策略交换，用 “D” 编码。每个帖子仅归类为一个类别。
3.3.2. 编码的可靠性
编码器间可靠性衡量编码器在每个分析单元的适当类别上相互同意的程度。一致性越高，内容分析的可靠性就越高。Cohen 的 κ 系数用作分类项目的编码器间一致性或编码器间可靠性的统计度量。最后，我们获得了 0.73 的 κ 分数，这表明编码过程的高可靠性（Eugenio 和 Glass，2004 年）。
3.4. 变量
我们定义了两个因变量：交互性的广度和交互性的深度。我们将分别探讨哪些因素会影响呼吸和互动的深度。
对于自变量，考虑了两种特征：结构特征、内容特征。 表 III 给出了变量的定义和作化。
4. 结果
4.1. 结构特征的影响
由于某些自变量的二分性质，采用一般线性模型 （GLM） 进行分析。与线性回归相比，GLM 通过考虑因变量之间的相关性具有更大的统计能力（Bray 和 Maxwell，1985）。GLM 方法可以看作是单个因变量的线性多元回归的扩展，GLM 与多元回归的不同之处在于可以分析的因变量的数量。在我们的研究中，有两个因变量，因此采用 GLM 来确定结构因素对两个因变量（广度和深度）的统计贡献。回归结果表明，该模型解释了更高的方差程度 （调整后的 R2 > 0.49）。结果如表 IV 所示。
结果表明，提及 “@” 函数的比率变量和标签 “#” 函数的比率对增强日均转发没有显著贡献，这意味着它们对交互性的广度维度没有显著影响。然而，多媒体元素的比例和外部链接的比例这两个变量都对交互性的广度有积极影响。结果还表明，多媒体特征的比例和原创性的比例对交互深度有积极影响。总体而言，结果表明多媒体在改善政府与公众之间的互动方面发挥着重要作用。
4.2. The impact of content features
To answer whether the content features influence the breadth and the depth of interactivity, the research first computes content-related statistics and features based on the content coding scheme. The study counts the average daily ratio of the forwarding number, the average daily ratio of comments, and the likes on the following categories, which are government-relevant (1), government-irrelevant (0), government affairs (A), public service (B), news report (C), and policy exchanges (D), respectively. Then the method of one-way ANOVA is used to test the significance as the Table V shows.
According to the analysis results, there is no significant difference between group 1 and group 0 in the ratio of daily forwarding, while there is a significant difference between Group 1 and Group 0 in the ratio of daily comments and likes. There is also no significant difference among groups A, B, C, D in a daily forwarding ratio, while there is significant difference among groups A, B, C, D in the average number of comments and likes.
5. Discuss of major findings and implications
The study is to explore the factors that can explain the degree of interactivity between governments and the general public in social media. Although the “best” way to use social media by governments is a nebulous and subjective problem that does not lend itself to a single set of guidelines for every task, country, agency, citizen, and government (Magro, 2012), we can find ways to strengthen the interactivity of government social media usage. We propose a new analytical framework based on communication theory. Based on the research framework, the concept of interactivity is divided into two sub-dimensions, which are breadth and depth. The factors influencing the interactivity are also divided along two dimensions, which are structural features and content features. We find that both structural features and content features impact the depth and breadth of interactivity.
5.1. Main findings
This study is one of the first research studies to examine the role of the structural features and content features in the social media usage of government organizations. The results indicate that the structural feature and content feature can explain the 49.9 and 58.6 per cent variance, respectively, which is a relatively high value. From the data analysis results, three key findings were derived.
First, the independent variable of the ratio of multimedia, belonging to the structural features, significantly impacts the interactivity, including depth and breadth of social media of government. The ratio of multimedia has a negative effect on depth (coefficient=−34.262368) and positive effect on breadth (coefficient=192.341435). This suggests that the increasing ratio of multimedia will reduce the number of forwards, and decrease the number of comments and likes. Multimedia is the combination of different types of media in the communication of information between users and their computers (Newton, 2001). The formats in which the information in the social media for communication exists differ, but they usually include voice communication, sound processing, data communication, telecommunication, and image processing. Based on research in cognitive science, human mind has limited capacity to process the information at any one time (Chandler and Sweller, 1991; Sweller, 1999; Baddeley, 1998). Adding multimedia to the post will enrich the contents to the citizens, and lead to overload in which some of the contents may not be processed. The citizens will not give their feedback, as likes and comments, to the posts. In contrast, forwarding is only a one-way communication, and is easy for the citizens to do, therefore, adding multimedia will increase the number of forwards.
Second, the ratio of external links significantly and positively impacts the breadth. An external link is a hyperlink that points to another website on the internet, typically on another domain from the current website. External links are important to web pages because they provide additional information and give a breadth of resources to follow. Obviously, more external links means more breadth, and more forwards. In practice, public administrations could plan a sensible use of external links to enlarge the information diffusion.
Third, the ratio of originality significantly and positively impacts the depth, which means the original post will receive more comments and likes. The majority of posts in governmental social media are the retweets and transmit messages from super administrative department to the general public. In this situation, it is a one-way communication. However, an original governmental post always conveys information in a peer to peer style, and is just like a conversion. The users in this dialogue will give more comments and likes than the others.
Fourth, the results indicate that increasing the number of external links in a post will receive more forwarding or resharing. This result also meets our experience. The post includes more the external link, more forwarding or resharing will be given to.
Last, the number of comments and likes of four different topic categories, including government-irrelevant, government affairs, public service, and news, is significantly different. Obviously, citizens have interests in different kinds of topics. Therefore, government should offer the topics that people like in order to improve the interactivity, especially the municipal affairs related, because the main purpose of the interaction with a government is concerning the governmental affairs. Furthermore, among the government-related posts, public service, and news report are closely related to people’s daily lives, which are the information that the ordinary citizens are expected to receive. This results in many comments and likes are given to these types of posts. The government should offer posts that are closely related to local affairs, especially public service information.
The main findings of the research are to give us further understanding of the breadth and depth in social media. The breadth means information diffusion, while the depth means conversation. The forwarding associated with breath can transmit message broadly, while the comments and likes associated with depth can provide valuable insight and emotional tendency into the original comments. The comments on the original text provided for clarity, go well with the interpersonal conversation.
5.2. Implications for social media analytics
This research also has an impact on social media analytics. The research has established a two-dimension framework to explain the interactivity of social media. The two dimensions, including depth and breadth, are helpful to explore the possible mechanism of interaction in social media. Furthermore, it’s also helpful for us to understand the interactive phenomenon under similar context.
In addition, the paper also has built up a general process for social media analytics from the perspective of content and structure. This process can be used to analyse other types of social media data, such as blogs (e.g. Blogger, LiveJournal), social networking sites (e.g. Facebook, LinkedIn), and multimedia sharing (Flickr, Youtube).
5.3. Implications for practice
Based on the research findings, we have some suggestions to improve the interactivity between governments and the general public. Government posts can be released with a multimedia format and add external links to facilitate information diffusion. In addition, public administrations should plan a sensible use of the original posts to inspire conversations from citizens, in which they are more likely to give feedbacks to the post to express the opinions.
From the perspective of governance, we suggest that government strengthens the interaction with the citizens by setting special topics on social media. Then the general public can be further deeply involved in these topics. The government can get feedbacks immediately by this channel, and can even influence public opinions.
6. Conclusion
In this study, an analytical framework to measure interactivity between governments and the public is established, and the empirical analysis method is employed to conduct an empirical test. The results show that social media could strengthen the interactivity between government and the general public through the structural features and the content features of posts. The results also enlighten that public administrations can improve the interactivity through managing structural factors and content factors.
Our paper has certain limitations. First, the sample size is relatively small and more variables from Table I could to be added in the future research. We collected the sample data by web crawler software developed by ourselves and processed the data manually. In the future, we can collect data and process them automatically by software tool kits, which will support a bigger data size. Second, we study only two categories of features related to social media posts. We can also include other new variables into our research in the future research. For instance, government styles for the implementation of environmental regulation might predict autonomous motivation so as to influence behaviour (Lavergne et al., 2010).
The interactivity of social media is only a slight step to the successful application. Social media application in e-government is necessary for the area of objectives and strategy, categorization of e-government applications, and policy making (Magro, 2012). More future research is required on the long-range plans for citizen participation and involvement.
About the authors
Xiaoling Hao is an Associate Professor of School of Information Management and Engineering in the Shanghai University of Finance and Economics. She received her Doctor Degree from the Tongji University, P.R. China, and her publication has appeared in Decision Support Systems, and other major Chinese academic journals. Her research interests include social media, text mining.
Daqing Zheng is an Associate Professor of School of Information Management and Engineering in the Shanghai University of Finance and Economics. He received PhD in Information Systems from the Management School, Fudan University, P.R. China, in 2007. His publication has appeared in European Journal of Information Systems, Pacific Asia Conference on Information Systems, and other major Chinese academic journals. His research interests focus on social media, e-government. Daqing Zheng is the corresponding author and can be contacted at: zheng.daqing@shufe.edu.cn
Qingfeng Zeng is currently an Associate Professor at the Shanghai University of Finance and Economics (SUFE) in Shanghai, China. He earned his PhD Degree in Information Systems from the Fudan University in China in 2005, and an MS in Information Systems from the Yunnan University in 2002, and a BS in Applied Mathematics from the Xiangtan University in 1999. His current research interests focus on social media analysis, e-business transformation of tradition enterprises, and online brand management, he has published more than 20 papers in journals and conferences proceedings. He has also been actively involved in management consulting projects for the Shanghai municipal government and a variety of companies in recent years.
Weiguo Fan is a Full Professor of Accounting and Information Systems and a Full Professor of Computer Science (courtesy) at the Virginia Polytechnic Institute and the State University (Virginia Tech). He received his PhD in Business Administration from the Ross School of Business, University of Michigan, Ann Arbor, in 2002, an MS in Computer Science from the National University of Singapore in 1997, and a BE in Information and Control Engineering from the Xi’an Jiaotong University, P.R. China, in 1995. His research interests focus on the design and development of novel information technologies – information retrieval, data mining, text/web mining, business intelligence techniques – to support better business information management and decision making. He has published more than 100 refereed journal and conference papers. His research has appeared in journals such as Information Systems Research, Journal of Management Information Systems, IEEE Transactions on Knowledge and Data Engineering, Information Systems, Communications of the ACM, Journal of the American Society on Information Science and Technology, Information Processing and Management, Decision Support Systems, ACM Transactions on Internet Technology, Pattern Recognition, IEEE Intelligent Systems, Pattern Recognition Letters, International Journal of e-Collaboration, and International Journal of Electronic Business.
References
Abbasi, A. , Chen, H. and Salem, A. (2008), “Sentiment analysis in multiple languages: feature selection for opinion classification in web forums”, ACM Transactions on Information Systems (TOIS) , Vol. 26 No. 3, pp. 55-59.
Abrahams, A.S. , Fan, W. , Wang, G.A. , Zhang, Z. and Jiao, J. (2015), “An integrated text analytic framework for product defect discovery”, Production & Operations Management , Vol. 24 No. 6, pp. 975-990.
Abrahams, A.S. , Jiao, J. , Fan, W. , Wang, G.A. and Zhang, Z. (2013), “What is buzzing in the blizzard of buzz: automotive component isolation in social media postings”, Decision Support Systems , Vol. 55 No. 4, pp. 871-882.
Baddeley, A. (1998), Human Memory , Allyn & Bacon, Boston, MA.
Balahur, A. and Steinberger, R. (2009), “Rethinking sentiment analysis in the news: from theory to practice and back”, in Troyano , Cruz and Díaz (Eds), Proceedings of Workshop on Opinion Mining & Sentiment Analysis, Seville, 13 November .
Bandari, R. , Asur, S. and Huberman, B. (2012), “The pulse of news in social media: forecasting popularity”, Sixth International AAAI Conference on Weblogs & Social Media, Trinity College, Dublin, 4-7 June.
Baum, C.H. and Maio, A.D. (2000), “Gartner’s four phases of e-government model”, available at: www.gartner.com/id=317292 (accessed 10 May 2015).
Bertot, J.C. , Jaeger, P.T. and Grimes, J.M. (2010a), “Using ICTs to create a culture of transparency: e-government and social media as openness and anti-corruption tools for societies”, Government Information Quarterly , Vol. 27 No. 3, pp. 264-271.
Bertot, J.C. , Jaeger, P.T. and Grimes, J.M. (2012a), “Promoting transparency and accountability through ICTs, social media, and collaborative e-government”, Transforming Government: People, Process and Policy , Vol. 6 No. 1, pp. 78-91.
Bertot, J.C. , Jaeger, P.T. and Hansen, D. (2012b), “The impact of polices on government social media usage: issues, challenges, and recommendations”, Government Information Quarterly , Vol. 29 No. 1, pp. 30-40.
Bertot, J.C. , Jaeger, P.T. , Munson, S. and Glaisyer, T. (2010b), “Engaging the public in open government: the policy and government application of social media technology for government transparency”, IEEE Computer , Vol. 43 No. 11, pp. 53-59.
Bray, J.H. and Maxwell, S.E. (1985), Multivariate Analysis of Variance , Sage Publications, Thousand Oaks, CA.
Carmel, D. , Roitman, H. and Yom-Tov, E. (2012), “On the relationship between novelty and popularity of user-generated content”, Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACM, pp. 565-582.
Chandler, P. and Sweller, J. (1991), “Cognitive load theory and the format of instruction”, Cognition and Instruction , Vol. 8 No. 4, pp. 293-332.
Chun, S.A. , Shulman, S. , Sandoval, R. and Hovy, E. (2010), “Government 2.0: making connections between citizens, data and government”, Information Polity , Vol. 15 No. 1, pp. 1-9.
Coursey, D. and Norris, D.F. (2008), “Models of e-government: are they correct? An empirical assessment”, Public Administration Review , Vol. 68 No. 3, pp. 523-536.
Criado, J.I. , Sandoval-Almazan, R. and Gil-Garcia, J.R. (2013), “Government innovation through social media”, Government Information Quarterly , Vol. 30 No. 4, pp. 319-326.
Downes, E.J. and McMillan, S.J. (2000), “Defining interactivity a qualitative identification of key dimensions”, New Media & Society , Vol. 2 No. 2, pp. 157-179.
Eugenio, B.D. and Glass, M. (2004), “The Kappa statistic: a second look”, Computational Linguistics , Vol. 30 No. 1, pp. 95-101.
Fan, W. and Gordon, M.D. (2014), “The power of social media analytics”, Communications of the ACM , Vol. 57 No. 6, pp. 74-81.
Ferro, E. , Loukis, E. , Charalabidis, Y. and Osella, M. (2013), “Analyzing the centralised use of multiple social media by government from innovations diffusion theory perspective”, 5th IFIP WG 8.5 International Conference, ePart 2013, Koblenz, 17-19 September , pp. 95-108.
Franzosi, R. (1989), “From words to numbers: a generalized and linguistics-based coding procedure for collecting textual data”, in Clogg, C. (Ed.), Sociological Methodology , Jossey-Bass, San Francisco, CA, pp. 263-298.
Gnyawali, D.R. , Fan, W. and Penner, J. (2010), “Competitive actions and dynamics in the digital age: an empirical investigation of social networking firms”, Information Systems Research , Vol. 21 No. 3, pp. 594-613.
Golbeck, J. , Grimes, J.M. and Rogers, A. (2010), “Twitter use by the US Congress”, Journal of the American Society for Information Science and Technology , Vol. 61 No. 8, pp. 1612-1621.
He, W. and Zha, S. (2014), “Insights into the adoption of social media mashups”, Internet Research , Vol. 24 No. 2, pp. 160-180.
Huhtamo, E. (1998), “From cybernation to interaction: a contribution to an archeology of interactivity”, in Lunenfeld, P. (Ed.), The Digital Dialectic: New Essays on New Media , MIT Press, Cambridge, MA, pp. 96-110..
Jonassen, D.H. , Beissner, K. and Yacci, M. (1993), Structural Knowledge: Techniques for Representing, Conveying, and Acquiring Structural Knowledge , Erlbaum Associates Publishers, Hove.
Kassen, M. (2013), “A promising phenomenon of open data: a case study of the Chicago open data project”, Government Information Quarterly , Vol. 30 No. 4, pp. 508-513.
Kietzmann, J.H. , Hermkens, K. , McCarthy, I.P. and Silvestre, B.S. (2011), “Social media? Get serious! Understanding the functional building blocks of social media”, Business Horizons , Vol. 54 No. 3, pp. 241-251.
Krippendorff, K. (2004), Content Analysis: An Introduction to its Methodology , 2nd ed., Sage Publications, Thousand Oaks, CA.
Kumar, R. and Tomkins, A. (2010), “A characterization of online browsing behavior”, Proceedings of the 19th International Conference on World Wide Web, ACM, Raleigh, NC, 26-30 April.
Kuzma, J. (2010), “Asian government usage of Web 2.0 social media”, European Journal of ePractice , Vol. 9 No. 3, pp. 1-13.
Lavergne, K.J. , Sharp, E.C. , Pelletier, L.G. and Holtby, A. (2010), “The role of perceived government style in the facilitation of self-determined and non self-determined motivation for pro-environmental behavior”, Journal of Environmental Psychology , Vol. 30 No. 2, pp. 169-177.
Lee, G. and Kwak, Y.H. (2012), “An open government maturity model for social media-based public engagement”, Government Information Quarterly , Vol. 29 No. 4, pp. 492-503.
Liang, Z. (2012), “Promoting the transformation of government functions through the government micro-blog”, News Window , Vol. 26 No. 5, pp. 32-33 (in Chinese).
Liu, D. , Huang, J. and Yan, W. (2013), “The study on the influence factors of enterprise micro-blog forwarding: from the perspective of form and content”, China Association for Information Systems (CNAIS), Guiyang, Guizhou, pp. 149-152.
Liu, Q. , Zhou, M. and Zhao, X. (2015), “Understanding news 2.0: a framework for explaining the number of comments from readers on online news”, Information & Management , Vol. 52 No. 7, pp. 764-776.
Livingstone, S. (2008), “Taking risky opportunities in youthful content creation: teenagers’ use of social networking sites for intimacy, privacy and self-expression”, New Media & Society , Vol. 10 No. 3, pp. 393-411.
McNutt, J.G. and Brainard, L.A. (2010), “Virtual government-citizen relations: informational, transactional, or collaborative?”, Administration & Society , Vol. 42 No. 7, pp. 836-858.
Magro, M. (2012), “A review of social media use in e-government”, Administrative Science , Vol. 2 No. 2, pp. 148-161.
Meijer, A. and Thaens, M. (2013), “Social media strategies: understanding the differences between North American police departments”, Government Information Quarterly , Vol. 30 No. 4, pp. 343-350.
Merchant, G. (2012), “Unravelling the social network: theory and research”, Learning, Media and Technology , Vol. 37 No. 1, pp. 4-19.
Mergel, I. (2013a), “A framework for interpreting social media interactions in the public sector”, Government Information Quarterly , Vol. 30 No. 4, pp. 327-334.
Mergel, I. (2013b), “Social media adoption and resulting tactics in the US Federal government”, Government Information Quarterly , Vol. 30 No. 2, pp. 123-130.
Newton, H. (2001), Newton’s Telecom Dictionary , CMP Books Publications, Halifax, Nova Scotia, CA.
Park, J. and Cho, K. (2009), “Declining relational trust between government and publics, and potential prospects of social media in the government public relations”, Proceedings of EGPA Conference 2009, The Public Service: Service Delivery in the Information Age, St. Julian ' s, Malta, 2-5 September.
Petrovic, S. , Osborne, M. and Lavrenko, V. (2011), “RT to win! Predicting message propagation in Twitter”, Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, 17-20 July.
Piller, F.T. , Vossen, A. and Ihl, C. (2012), “From social media to social product development: the impact of social media on co-creation of innovation”, Die Unternehmung , Vol. 65 No. 1, pp. 1-22.
Roberts, C. (2000), “A conceptual framework for quantitative text analysis”, Quality & Quantity , Vol. 34 No. 3, pp. 259-274.
Rogers, E.M. (1986), Communication Technology: The New Media in Society , The Free Press, New York, NY.
Sobkowicz, P. , Kaschesky, M. and Bouchard, G. (2012), “Opinion mining in social media: modeling, simulating, and forecasting political opinions in the web”, Government Information Quarterly , Vol. 29 No. 4, pp. 470-479.
Stromer-Galley, J. (2000), “Online interaction and why candidates avoid it”, Journal of Communication , Vol. 50 No. 4, pp. 111-132.
Suen, I.-S. (2006), “Assessment of the level of interactivity of e-government functions”, Journal of e-Government , Vol. 3 No. 1, pp. 29-51.
Suh, B. , Hong, L. , Pirolli, P. and Chi, E.H. (2010), “Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network”, 2010 IEEE Second International Conference on Social Computing (Socialcom), IEEE, Minneapolis, MN, 20-22 August.
Susanto, T.D. and Goodwin, R. (2013), “User acceptance of SMS-based e-government services: differences between adopters and non-adopters”, Government Information Quarterly , Vol. 30 No. 4, pp. 486-497.
Sweller, J. (1999), Instructional Design in Technical Areas , ACER Press, Camberwell.
Tang, Q. , Gu, B. and Whinston, A.B. (2012), “Content contribution for revenue sharing and reputation in social media: a dynamic structural model”, Journal of Management Information Systems , Vol. 29 No. 2, pp. 41-75.
Tsai, F.S. (2011), “A tag-topic model for blog mining”, Expert Systems with Applications , Vol. 38 No. 5, pp. 5330-5335.
Van Dijk, J. (2006), The Network Society: Social Aspects of New Media , Sage, Thousand Oaks, CA.
Walther, J.B. , Gay, G. and Hancock, J.T. (2005), “How do communication and technology researchers study the internet?”, Journal of Communication , Vol. 55 No. 3, pp. 632-657.
Wang, X. , Wei, F. , Liu, X. , Zhou, M. and Zhang, M. (2011), “Topic sentiment analysis in Twitter: a graph-based hashtag sentiment classification approach”, Proceedings of the 20th ACM International Conference on Information and Knowledge Management, ACM.
Welch, E.W. and Fulla, S. (2005), “Virtual interactivity between government and citizens: the Chicago police department’s citizen ICAM application demonstration case”, Political Communication , Vol. 22 No. 2, pp. 215-236.
Wikipedia (2008), “Data link”, available at: https://en.wikipedia.org/wiki/Data_link (accessed 10 May 2015).
Zhang, L. , Peng, T.-Q. , Zhang, Y.-P. and Wang, X.-H. (2014), “Content or context: which matters more in information processing on microblogging sites”, Computers in Human Behavior , Vol. 31 No. 2, pp. 242-249.
Zhao, F. (2011), “Impact of national culture on e-government development: a global study”, Internet Research , Vol. 21 No. 3, pp. 1-7.
Acknowledgements
This work is supported by the Project of National Natural Sciences Foundation of China (Grant No. 71301096, 71401096) and Shanghai Municipal Natural Science Foundation (Grant No. 13ZR1413400).