DiffuScope: inferring post-specific diffusion network
DiffuScope: inferring post-specific diffusion network
复制标题
DOI：
10.1145/3487351.3490967
复制
发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Md Rashidul Hasan;Dheeman Saha;F. A. Chowdhury;J. Degnan;A. Mueen
中科院分区：
文献类型：
--
作者：
Md Rashidul Hasan;Dheeman Saha;F. A. Chowdhury;J. Degnan;A. Mueen
特定于帖子的扩散网络阐明了社交媒体上帖子的谁看到谁的路径。针对特定帖子的扩散网络可以揭示用户之间的可信和/或激励的连接。不幸的是，这样的网络无法从社交媒体平台的可用信息中观察到;因此需要一种推理机制。在本文中，我们提出了一种算法来推断后的扩散网络，利用时间，文本和网络模态。该算法使用条件点过程识别最大似然扩散网络。该算法可以从单个帖子扩展到数千个共享，并且可以作为实时分析工具实现。我们分析了推断的扩散网络，并显示了不同用户群体（即验证与未经验证，保守与自由）和当地社区（政治，企业等）之间的信息扩散的明显差异。我们发现推断网络中的差异显示出自动机器人不成比例的存在，这是衡量帖子真实影响的一种潜在方法。
Post-specific diffusion network elucidates the who-saw-from-whom paths of a post on social media. A diffusion network for a specific post can reveal trustworthy and/or incentivized connections among users. Unfortunately, such a network is not observable from available information from social media platforms; hence an inference mechanism is needed. In this paper, we propose an algorithm to infer the diffusion network of a post, exploiting temporal, textual, and network modalities. The proposed algorithm identifies the maximum likely diffusion network using a conditional point process. The algorithm can scale up to thousands of shares from a single post and can be implemented as a real-time analytical tool. We analyze inferred diffusion networks and show discernible differences in information diffusion within various user groups (i.e. verified vs. unverified, conservative vs. liberal) and across local communities (political, entrepreneurial, etc.). We discover differences in inferred networks showing disproportionate presence of automated bots, a potential way to measure the true impact of a post.