README.md

Detecting Inspiring Content on Social Media

Oana Ignat, Y-Lan Boureau, Jane Yu, Alon Halevy

This repository contains the dataset and code for our paper, published at the International Conference on Affective Computing and Intelligent Interaction (ACII) 2021:

Detecting Inspiring Content on Social Media and free version on Arxiv.

Task Description

The goal of this research is to automatically recognize whether a post on social media is likely to inspire it's readers. We also annotate what effect the inspiring posts have on the reader: makes them feel good, motivates them to act, both or no effect, and what emotions they produce for the reader.

Data

• The inspirational posts: data_inspir.csv

• The submission/post ids of the inspiring and non-inspiring posts we collect for our paper: data_all_post_ids.csv

• The AMT annotations for emotions and reasons for the inspiring posts, from all 3 annotators per post: annotations_inspir_data

 "18ypya": {
    "reasons": {
      "feel good": 1,
      "influence": 1,
      "none": 1
    },
    "emotions": {
      "curiosity": 2,
      "gratitude": 1,
      "optimism": 1,
      "other: goals": 1
    },
...

• Each post is annotated by 3 annotators: we collect each of the annotated emotions/ reasons selected by the annotators and by how many annotators was selected. The annotators also have the option of adding other emotions/ reasons: they appear in our data starting with "other: ".

• We cannot share the text posts, only the post ids, because of privacy concerns. However, you can download all the post information from the post ids using a reddit crawler (pushift or praw). We show below an example using pushift.

Data Crawler

The code for crawling Reddit: collect_reddit_data.ipynb

Annotation Details

We filter the data using the following heuristics:

1. public posts with at least one comment that contains the substrings inspir or uplift (Reddit & Facebook)
2. public posts that authors mark as feeling inspired or feeling up (Facebook)
3. public posts that are shared at least 10 times (Facebook)
4. public posts from the subreddits that contain the substrings inspir or uplift (Reddit)
5. comments to the following four questions from the AskReddit subreddit:
   When was the last time you felt inspired?, Who or what inspired you?, Who inspired you and how?, What is the most inspiring thing you have ever seen or heard? (Reddit).

As control, we also collect random posts:

1. posts with no comment that contains the substrings inspir or uplift (Reddit & Facebook)
2. posts from random subreddits that do not contain the substrings inspir or uplift (Reddit)

The resulting posts are annotated by crowd-sourced workers to determine:

1. whether the post is inspiring or not;
2. if the post is inspiring, what influence it has on the reader;
3. what emotions it evokes;
4. the annotator's confidence in the answer.

Citation information

If you use this dataset or any ideas based on the associated research article, please cite the following:

@INPROCEEDINGS {9597431,
author = {O. Ignat and Y. Boureau and J. A. Yu and A. Halevy},
booktitle = {2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII)},
title = {Detecting Inspiring Content on Social Media},
year = {2021},
volume = {},
issn = {},
pages = {1-8},
keywords = {affective computing;social networking (online);psychology;transforms;machine learning;media;linguistics},
doi = {10.1109/ACII52823.2021.9597431},
url = {https://doi.ieeecomputersociety.org/10.1109/ACII52823.2021.9597431},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {oct}
}