Sharing social media data: The role of past experiences, attitudes, norms, and perceived behavioral control

Akdeniz, Esra; Borschewski, Kerrin; Breuer, Johannes; Voronin, Yevhen

doi:10.3389/fdata.2022.971974

Cited by 4 publications

(3 citation statements)

References 66 publications

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“…Our study is also in line with Akdeniz et al (2023), who found that while altruistic motives like contributing to open science and transparency were pivotal in sharing decisions, practical concerns like legal restrictions and ethical considerations also played a significant role. Such insights are crucial in understanding the dynamics of data-sharing behavior.…”

Section: Attitudinal Factorssupporting

confidence: 90%

“…For example, Sommestad et al (2015) used the TPB to explain policy compliance intentions in information security and showed that the TPB made good predictions. Akdeniz et al (2023) used the TPB and identified critical factors influencing intentions to share social media data, including past experiences with data sharing, attitudes towards sharing, perceived norms, and perceived behavioral control.…”

Section: The Theory Of Planned Behaviormentioning

confidence: 99%

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Attitudinal, Normative, and Resource Factors Affecting Communication Scholars’ Data Sharing: A Replication Study

Xu,

Zhang

2024

MaC

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This study explores the factors affecting communication scholars’ data-sharing intentions, a critical component of reproducibility and replicability in open science. We replicate Harper and Kim’s (2018) study, which employs the theory of planned behavior to demonstrate the impacts of attitudinal, normative, and resource factors. Specifically, their original research examines data-sharing practices among psychologists, and our replication aims to reinforce their findings within the communication field. Data from a survey of Chinese communication scholars (<em>N</em> = 351) are analyzed using structural equation modeling. The findings indicate that perceived benefit and perceived risk significantly influence the attitudes of communication scholars towards sharing their data, positively and negatively, respectively. Additionally, attitudes, subjective norms, journal pressure, and the conditions facilitating data sharing have a significant positive impact on communication scholars’ behavioral intentions. Perceived effort inversely affects attitudes toward data sharing but does not impact behavioral intentions. This study provides a theoretical framework for understanding data-sharing intentions and behaviors in the open science movement. The role of this research as a replication study serves as a compelling demonstration of scientific inquiry. Practical suggestions, such as fostering open dialog, institutional incentives, and cooperation between different actors to increase communication scholars’ data-sharing intentions, and recommendations for carrying out replication and reproduction studies, are discussed. Finally, we judiciously reflect on the methodological limitations of our research and highlight directions for future research on open science.

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Section: Attitudinal Factorssupporting

confidence: 90%

Section: The Theory Of Planned Behaviormentioning

confidence: 99%

Attitudinal, Normative, and Resource Factors Affecting Communication Scholars’ Data Sharing: A Replication Study

Xu,

Zhang

2024

MaC

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“…Compared with data obtained from traditional surveys, tourist attraction name data on social media not only provide the possibility of researching a wide range of topics but also allow for the investigation of research questions with large samples. Compared with survey data, they can be monitored continuously for a long time (and with high temporal resolution) at a lower cost [6]. After analyzing and being inspired by previous research, this paper proposes a Chinese tourist attraction toponym extraction model based on RoBERTa-BiLSTM-CRF [7].…”

Section: Introductionmentioning

confidence: 99%

A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions

Zhang

et al. 2023

IJGI

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In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition.

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Key topic or bare necessity? How Research Ethics are Addressed and Discussed in Computational Communication Science

Knöpfle,

Haim,

Breuer

2024

Publizistik

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In Computational Communication Science (CCS) researchers grapple with intricate ethical challenges arising from the collection and analysis of complex data sets, often including sensitive or copyrighted data. Taking into consideration differences between the two main lines of philosophical reasoning in the realm of (research) ethics—deontology and consequentialism—we argue that ethical challenges faced in CCS are multidimensional and, hence, require multiple perspectives and approaches. Our general considerations are complemented by an empirical study that aims to assess the nature, prevalence, and discussion of ethical issues in CCS literature. Through a manual content analysis of 476 CCS publications, we shed light on ethical challenges as well as reflections thereof by CCS researchers. Notably, we find that only 5.88% of studies explicitly address general ethical considerations. Ethical review processes are also only mentioned in 6.51% of the considered studies, with the majority focusing on specific ethical procedures, such as obtaining informed consent, data anonymization measures, or debriefing. This suggests that, in the absence of consensus and field-specific standards, researchers in CCS prioritize context-specific ethical procedures, emphasizing the importance of flexibility in addressing ethical considerations.

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Sharing social media data: The role of past experiences, attitudes, norms, and perceived behavioral control

Cited by 4 publications

References 66 publications

Attitudinal, Normative, and Resource Factors Affecting Communication Scholars’ Data Sharing: A Replication Study

Attitudinal, Normative, and Resource Factors Affecting Communication Scholars’ Data Sharing: A Replication Study

A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions

Key topic or bare necessity? How Research Ethics are Addressed and Discussed in Computational Communication Science

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