This project was carried out by Communication Studies students at VU University Amsterdam for the course "Digital Humanities and Social Analytics in Practice". All data and code are for a scientific purpose. Description of the files in the data_and_code_What_makes_users_click_project.zip file: original_data upworthy-archive-confirmatory-packages-03.12.2020.csv: original dataset downloaded from The Upworthy Research Archive (https://osf.io/jd64p/) cleaned_data notebook_clean_data.ipynb: Python code for cleaning the original data cleaned_data.csv: cleaned data file sample_data notebook_sample_manual_annotations.ipynb: Python code to draw a sample from the cleaned data file sample_manual_annotations.csv: sample data file inter_annotator_study inter-annotations_and_gold_annotations.csv: file with values of the two annotators and the gold values notebook_inter-annotator_scores_confusion_matrix.ipynb: Python code to calculate inter-annotator scores and create a confusion matrix inter-annotator_sentiment.csv: input file with values of the two annotators for the label sentiment inter-annotator_emotion.csv: input file with values of the two annotators for the label emotion annotating_sample_data lexicon NRC-Emotion-Lexicon-Wordlevel-v0.92.txt: NRC Emotion Lexicon used notebook_sentiment_emotion_test_set.ipynb: Python code to annotate headlines in sample data file input_sentiment.tsv: input file with gold values for the label sentiment for the headlines in de sample data file input_emotion.tsv: input file with gold values for the label emotion in de sample data file final_data notebook_merge_dataframes.ipynb: Python code to merge cleaned data file with output files of the sentiment and emotion analyses final_dataset.csv: final data file annotating_final_data lexicon NRC-Emotion-Lexicon-Wordlevel-v0.92.txt: NRC Emotion Lexicon used notebook_sentiment_emotion_final_annotation.ipynb: Python code to annotate headlines in final data file input_all_headlines.tsv: input file with all headlines of the final data file data_analyses notebook_descriptive_statistics.ipynb: Python code to obtain descriptive statistics multilevel_regression_analysis.R: R code to perform regression analyses