Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia
Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia
复制标题
维基百科中自动化软件代理之间的冲突与合作的运作
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Aaron L Halfaker
中科院分区：
文献类型：
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作者：
R. Geiger;Aaron L Halfaker
本文重复、扩展和驳斥了发表在《公共科学图书馆·综合》(《即使好的机器人战斗》)上的一项研究中的结论，该研究声称使用纯粹的定量方法识别维基百科中自动软件代理(或机器人)之间的实质性冲突。通过应用一种综合的混合方法，借鉴痕迹民族志，我们将这些被指控的BOT-BOT冲突案例置于背景中，并更好地理解这些相互作用。我们发现，压倒性地，以前被描述为有问题的冲突实例的互动通常更好地被描述为例行公事、富有成效的，甚至是协作工作。这些结果挑战了过去的工作，并表明了定性/定量合作的重要性。在本文中，我们提出了用于实现BOT-BOT冲突的定量度量和定性启发式方法。我们对BOT-BOT恢复时出现的各种事件进行了详细的描述，以帮助区分冲突和非冲突。我们通过编辑摘要中的模式对这些类型的事件进行计算分类。通过在人们赋予数据意义的社会技术背景下解释发现/跟踪数据，我们从定量测量中获得更多，对维基百科中算法系统的治理有了更深的理解。我们还发布了我们的数据收集、处理和分析管道，以促进我们的研究结果的计算重复性，并帮助其他有兴趣在其他平台和背景下进行类似混合方法研究的研究人员。
This paper replicates, extends, and refutes conclusions made in a study published in PLoS ONE ("Even Good Bots Fight"), which claimed to identify substantial levels of conflict between automated software agents (or bots) in Wikipedia using purely quantitative methods. By applying an integrative mixed-methods approach drawing on trace ethnography, we place these alleged cases of bot-bot conflict into context and arrive at a better understanding of these interactions. We found that overwhelmingly, the interactions previously characterized as problematic instances of conflict are typically better characterized as routine, productive, even collaborative work. These results challenge past work and show the importance of qualitative/quantitative collaboration. In our paper, we present quantitative metrics and qualitative heuristics for operationalizing bot-bot conflict. We give thick descriptions of kinds of events that present as bot-bot reverts, helping distinguish conflict from non-conflict. We computationally classify these kinds of events through patterns in edit summaries. By interpreting found/trace data in the socio-technical contexts in which people give that data meaning, we gain more from quantitative measurements, drawing deeper understandings about the governance of algorithmic systems in Wikipedia. We have also released our data collection, processing, and analysis pipeline, to facilitate computational reproducibility of our findings and to help other researchers interested in conducting similar mixed-method scholarship in other platforms and contexts.