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Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Jie Zou, Aixin Sun, Cheng Long, Mohammad Aliannejadi, Evangelos Kanoulas
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Yeting Guo, Fang Liu, Tongqing Zhou, Zhiping Cai, Nong Xiao
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Wei Zhuang, Qingfeng Zeng, Yu Zhang, Chunmei Liu, Weiguo Fan
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Meng Cai, Han Luo, Xiao Meng, Ying Cui, Wei Wang
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Ziqiao Ao, Gergely Horváth, Chunyuan Sheng, Yifan Song, Yutong Sun
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Xixi Zhou, Jiajun Bu, Sheng Zhou, Zhi Yu, Ji Zhao, Xifeng Yan
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Maud Reveilhac, Gerold Schneider
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Jie Nie, Ting Zhang, Tianbao Li, Shusong Yu, Xuanya Li, Zhiqiang Wei
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Mingxin Gan, Yingxue Ma
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Ming Xie, Xiu-Xiu Zhan, Chuang Liu, Zi-Ke Zhang
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Lili Shang, Hao Xi, Jiaojiao Hua, Huayun Tang, Jilei Zhou
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Zheng Zhang, Jun Wan, Mingyang Zhou, Zhihui Lai, Claudio J. Tessone, Guoliang Chen, Hao Liao
Publication date: April 2023
Source: International Journal of Information Management, Volume 69
Author(s): Pradeep Kumar, Sujeet Kumar Sharma, Vincent Dutot
Publication date: February 2023
Source: International Journal of Information Management, Volume 68
Author(s):
Political polarisation has become an increasingly alarming issue in society, exacerbated by the widespread use of social media and the development of filter bubbles among social media users. This environment has left users susceptible to disinformation, especially those with whom a user is politically aligned. In this research, we integrate truth bias, elaboration likelihood model and new media literacy into a model for explaining social media engagement (with both disinformation and factual information) and analysing how political polarisation (operationalised as political alignment between users) influences perceptions and behaviours. Using an experimental design, we analyse the model separately for posts containing disinformation and factual information, highlighting key differences. Political alignment positively moderates truth bias's effect on engagement with disinformation. For both disinformation and factual information, political alignment moderates the effect of generalised communicative suspicion (GCS) on truth bias, such that GCS's effect on truth bias flips from negative to positive as political alignment increases. Issue involvement and political alignment appear to be the primary drivers of disinformation engagement, with critical consuming media literacy failing to mitigate engagement. Our findings contribute to the understanding of persuasion, conviction, amplification, polarisation and aversion related to fake news on social media.
Publication date: April 2023
Source: International Journal of Information Management, Volume 69
Author(s): Shijiao (Joseph) Chen, Khai Trieu Tran, Zhenhua (Raymond) Xia, Donia Waseem, Jing A. Zhang, Balkrushna Potdar
Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the associated metadata is often sparse and, at times, erroneous. This paper extends previous research conducted with the Illinois Natural History Survey (INHS) collection (7244 specimen images) that uses computational approaches to analyze image quality, and then automatically generates 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of the Wisconsin Zoological Museum (UWZM) collection (4155 specimen images). Further, we enhance our computational methods in four ways: (1) augmenting the training set, (2) applying contrast enhancement, (3) upscaling small objects, and (4) refining our processing logic. Together these new methods improved our overall error rates from 4.6 to 1.1%. These enhancements also allowed us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories world-wide by generating accurate and valuable metadata for those repositories.
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Karol Nowakowski, Michal Ptaszynski, Kyoko Murasaki, Jagna Nieuważny
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Kelsey Urgo, Jaime Arguello
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Qian Liu, Rui Mao, Xiubo Geng, Erik Cambria
Publication date: January 2023
Source: Information Processing & Management, Volume 60, Issue 1
Author(s):
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Guolong Wang, Xun Wu, Zhaoyuan Liu, Zheng Qin
Publication date: January 2023
Source: Information Processing & Management, Volume 60, Issue 1
Author(s): V Adarsh, P Arun Kumar, V Lavanya, G.R. Gangadharan
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Peyman Rostami, Azadeh Shakery
Publication date: March 2023
Source: Information Processing & Management, Volume 60, Issue 2
Author(s): Haythem Bany Salameh, Mohannad Alhafnawi, Ala’eddin Masadeh, Yaser Jararweh
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