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Whose posts to read: Finding social sensors for effective information acquisition

机译：谁读书：寻找社会传感器，以获得有效信息收购

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摘要

In the era of big data, it is extremely challenging to decide what information to receive and filter out in order to effectively acquire high-quality information, particularly in social media where large-scale User Generated Contents (UGC) is widely and quickly disseminated. Considering that each individual user in social network can take actions to drive the process of information diffusion, it is naturally appealing to aggregate spreading information effectively at the individual level by regarding each user as a social sensor. Along this line, in this paper, we propose a framework for effective information acquisition in social media. To be more specific, we introduce a novel measurement, the preference-based Detection Ability to evaluate the ability of social sensors to detect diffusing events, and the problem of effective information acquisition is then reduced to achieving social sensing maximization through discovering valid social sensors. In pursuit of social sensing maximization, we propose two algorithms to resolve the longstanding problems in traditional greedy methods from the perspectives of efficiency and performance. On the one hand, we propose an efficient algorithm termed LeCELF, which resolves the redundant re-evaluations in the traditional Cost-Effective Lazy Forward (CELF) algorithm. On the other hand, we observe the participation paradox phenomenon in the social sensing network, and proceed to propose a randomized selection-based algorithm called FRIENDOM to choose social sensors to improve the effectiveness of information acquisition. Experiments on a disease spreading network and real-world microblog datasets have validated that LeCELF greatly reduces the running time, whereas FRIENDOM achieves a better detection performance. The proposed framework and corresponding algorithms can be applicable in many other settings in resolving information overload problems.
机译：在大数据的时代，决定接收和过滤的信息是非常具有挑战性的，以便有效地获取高质量信息，特别是在大规模用户生成内容（UGC）的社交媒体上广泛和快速传播。考虑到社交网络中的每个用户可以采取动作来驱动信息扩散的过程，通过将每个用户视为社交传感器，自然吸引到各个电平的聚合信息。在这篇文章中，在本文中，我们向社交媒体中提出了一个有效信息获取的框架。为了更具体地，我们引入了一种新的测量，基于偏好的检测能力来评估社交传感器检测扩散事件的能力，然后减少了有效信息采集的问题，以通过发现有效的社交传感器来实现社会感官最大化。为了追求社会感知最大化，我们提出了两种算法，解决了传统贪婪方法中的长期存在的问题，从效率和性能的角度来看。一方面，我们提出了一种被称为LECelf的有效算法，这可以解决传统的经济高效懒人前进（Celf）算法中的冗余重新评估。另一方面，我们遵守社会传感网络中的参与悖论现象，并继续提出一种被称为FERMEM的基于随机选择的算法，以选择社会传感器，以提高信息获取的有效性。对疾病传播网络和现实世界微博数据集的实验已经验证了Lecelf大大减少了运行时间，而Fedrierom达到了更好的检测性能。建议的框架和相应的算法可以在解决信息过载问题中的许多其他设置中适用。

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