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SpaceTourismDataResearch
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SpaceTourismData

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The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 250, in _split_generators
                  raise ValueError(
                      "`file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files"
                  )
              ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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SpaceTourismData

Dataset Description

SpaceTourismData is a multimodal social media dataset developed for research on consumer engagement with space tourism content.

The dataset supports the study:

“Processing Fluency in Space Tourism Social Media: How Visual–Textual Congruence Drives Consumer Engagement.”

It contains two main components:

Study 1 raw multimodal data, including social media images, captions, and associated metadata.
Study 2 regression data, containing the variables used for the empirical regression analyses.

The dataset is intended to support reproducibility and further research on multimodal social media analysis, space tourism communication, consumer engagement, visual–textual congruence, and explainable artificial intelligence (XAI).

Repository Structure

SpaceTourismData/
│
├── Study1_rawdata/
│   ├── images/
│   ├── raw/
│   ├── captions.csv
│   ├── captions_merged.csv
│   ├── metadata.jsonl
│   └── summary.json
│
└── Study2_regression_sample_1057.csv

`Study1_rawdata/`

This directory contains the raw multimodal social media data used in the data-preparation and multimodal-analysis pipeline.

The directory includes:

images/: collected image files;
raw/: raw data generated during data collection;
captions.csv: caption information associated with the collected posts;
captions_merged.csv: processed/merged caption data;
metadata.jsonl: post-level metadata;
summary.json: summary statistics describing the data collection.

The collection contains posts associated with major space tourism companies, including Virgin Galactic, Blue Origin, and Axiom Space, as well as additional accounts appearing in the raw collection.

Video content was excluded from the image-based analytical pipeline where applicable.

Study 1: Multimodal Data

Study 1 uses social media images and captions for multimodal analysis.

The multimodal modeling framework was designed to examine how visual and textual information jointly relates to consumer engagement. Explainable AI techniques were used to identify visual and textual features associated with predicted engagement.

The Study 1 files are provided primarily to facilitate:

multimodal image–text analysis;
replication of data preprocessing;
visual feature extraction;
text feature extraction;
image–caption alignment analysis;
explainable AI analysis; and
development of alternative multimodal engagement models.

Because Study1_rawdata/ is a raw-data archive, the files contained in this directory should not necessarily be interpreted as the final analytical sample without applying the corresponding preprocessing and sample-selection procedures.

Study 2: Regression Dataset

`Study2_regression_sample_1057.csv`

This file contains the final regression sample used in Study 2.

Number of observations: 1,057

Each row represents one social media post. The dataset integrates engagement outcomes, experiential content measures, image–text congruence, visual characteristics, multimodal indicators, and control variables.

Main Variables

Variable	Description
`Y`	Consumer engagement measured by the number of likes
`TEC`	Transformative Experience Cues
`OEC`	Ordinary Experiential Cues
`NEC`	Non-Experiential/Informational Cues
`TIC`	Text–Image Congruence
`VC`	Visual Concreteness
`TEC_x_TIC_centered`	Interaction between mean-centered TEC and TIC
`TEC_x_VC_centered`	Interaction between mean-centered TEC and VC

Textual Indicators

Variable	Description
`T1`	Caption length
`T1_log1p`	Log-transformed caption length, ln(T1 + 1)
`T2`	Frequency of specific keywords
`T3`	Frequency of space-related terms

Multimodal / XAI Indicators

Variable	Description
`M1`	Presence of objects associated with higher predicted engagement in Study 1
`M2`	Presence of backgrounds associated with higher predicted engagement in Study 1
`M3`	Presence of objects associated with lower predicted engagement in Study 1
`M4`	Presence of backgrounds associated with lower predicted engagement in Study 1

Control Variables

Variable	Description
`C1`	Days since posting
`C1_log1p`	Log-transformed days since posting, ln(C1 + 1)
`C2`	Number of detected objects
`C3`	Image brightness
`C4`	Company
`C4_BlueOrigin`	Indicator for Blue Origin
`C4_AxiomSpace`	Indicator for Axiom Space
`C5`	Media format
`C6`	Number of hashtags
`C7`	Number of mentions
`C8`	Year-month indicator used for year-month fixed effects

For the company indicators, Virgin Galactic serves as the reference category in the regression models.

Research Use

The dataset may be useful for research in areas including:

space tourism;
consumer engagement;
social media analytics;
multimodal machine learning;
visual–textual congruence;
processing fluency;
experiential marketing;
computer vision;
natural language processing;
explainable artificial intelligence; and
computational marketing research.

The Study 2 regression dataset can also be used to reproduce or extend the statistical analyses reported in the associated research.

Data Source and Processing

The source material consists of publicly accessible social media content collected for academic research.

The raw-data archive contains images, captions, and post metadata. The research pipeline subsequently transforms the raw multimodal information into theoretically motivated and computationally derived variables for statistical analysis.

For Study 2, image- and text-based indicators are combined with independently measured experiential cues, text–image congruence, visual concreteness, and control variables.

Users seeking to reproduce the analyses should distinguish between the raw multimodal archive in Study1_rawdata/ and the processed post-level analytical dataset in Study2_regression_sample_1057.csv.

Important Notes

This dataset was prepared for academic research and reproducibility.

Social media content may remain subject to the rights of the original content creators and to the applicable terms and policies of the original platform. Users of this dataset are responsible for ensuring that their use of the data complies with applicable laws, ethical requirements, platform policies, and third-party rights.

The repository metadata is distributed under the Apache License 2.0. This license should not be interpreted as transferring ownership of third-party social media content.

Citation

If you use this dataset in academic research, please cite the associated study and dataset.

Associated Study

Processing Fluency in Space Tourism Social Media: How Visual–Textual Congruence Drives Consumer Engagement.

Full bibliographic information can be added after publication.

License

Repository metadata and researcher-generated materials are released under the Apache License 2.0, unless otherwise specified.

Third-party social media images, captions, trademarks, and other externally created materials remain subject to their respective rights holders and applicable platform terms.

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