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Looking for trouble: A multilevel analysis of disagreeable contacts in online social networks

机译:寻找问题:在线社交网络中不良联系人的多层次分析

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Identifying characteristics of troublemakers in online social networks, those contacts who violate norms via disagreeable or unsociable behaviour, is vital for supporting preventative strategies for undesirable, psychologically damaging online interactions. To date characterising troublemakers has relied on self reports focused on the network holder, largely overlooking the role of network friends. In the present study, information was obtained on 5113 network contacts from 52 UK-based Facebook users (age range 13-45; 75% female) using digitally derived data and in-depth network surveys. Participants rated their contacts in terms of online disagreement, relational closeness and interaction patterns. Characteristics of online troublemakers were explored using binary logistic multilevel analysis. Instances of online disagreement were most apparent in the networks of emerging adults (19-21 years). Contacts were more likely to be identified as online troublemakers if they were well connected within the network. Rates of offline and Facebook exchanges interacted such that contacts known well offline but with low rates of Facebook communication were more likely to be identified as troublemakers. This may indicate that users were harbouring known troublemakers in a bid to preserve offline relationships and reputational status. Implications are discussed in terms of an individual's susceptibility to undesirable encounters online. (C) 2017 Elsevier Ltd. All rights reserved.
机译:识别在线社交网络中制造麻烦的人的特征,即那些通过令人讨厌或无法交际的行为违反规范的联系人,对于支持针对不良的,心理上有害的在线互动的预防策略至关重要。迄今为止,刻画麻烦制造者的特征依赖于针对网络所有者的自我报告,而在很大程度上忽略了网络朋友的角色。在本研究中,使用数字衍生数据和深入的网络调查,从52位英国Facebook用户(年龄在13-45岁;女性占75%)获得了5113个网络联系人的信息。参与者根据在线意见分歧,关系亲密程度和互动方式对他们的联系人进行评分。使用二进制逻辑多级分析探索了在线麻烦制造者的特征。在线意见分歧的案例在新兴成年人网络(19-21岁)中最为明显。如果联系人在网络中连接良好,则更有可能被确定为在线麻烦制造者。脱机率和Facebook交流率相互作用,因此,脱机状态众所周知但Facebook通信率较低的联系人更有可能被认为是制造麻烦的人。这可能表明用户在保留已知的麻烦制造者,以维护离线关系和声誉。根据个人对在线不良遭遇的敏感性来讨论影响。 (C)2017 Elsevier Ltd.保留所有权利。

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