---
# System prepended metadata

title: Research Questions

---

Abbreviations used throughout 

- minimal (M)
- typical (T)
- extended (E)
- CT conspiracy theories

## Motivation, Implications, Gap, Novelty

## Research Questions

1. M Which sub-groups of conspiracy theory users emerge from their interactions? 
2. M How do such groups differ in terms of the sociodemographic attributes of the users?
3. T How do they differ in terms of the conspiracy theories they discuss?
4. E To what degree are the interactions between conspiracy theory users statistically related to their sociodemographic and topical homophily?

## Assumptions and Hypotheses

## Data

Data sources: convokit and waller cover 2005--2018

- M r/conspiracy, January--June 2018
- T 56 subreddits from the list in [[@phadke2021]], January--June 2018
- E 2005--2018 or August 2021, plus exploratively add subreddits that might fit

## Features

### Sociodemographics
- M age, gender, affluence, political leaning from [[@waller2021]]
- T 
- E + correlates of conspiracy thinking (academic education, ...) ==todo== review other indicators that are supposedly associated with CT

### Topics
- M 
- T BERTopics
- E Named Entities/Subject-Verb-Object triples

## Analyses
- M
    - reply interaction network
    - community detection on the interaction network
    - sensitivity analysis for the community detection
    - topic extraction
    - topic interpretation
    - distribution of topics per user
    - distribution of topics per cluster
    - statitical analyses of the differences of topics per cluster
- T
    - reply network by subreddit
    - BERTopics
- E
    - validation of the newly extracted dimensions with self-disclosures and correlation with external ground truth on locations
    - dimensionality reduction of the bipartite user-NE network
    - logistic regression models




----
## Steps
1. collect reddit comment data via subreddits
	- minimally: r/conspiracy
	- standard: list 56 subreddits
	- extended: exploratively add subreddits that might fit
3. extract reply network from comments (minimally: for 1 month in 2018; standard: for 2005--2018)
	1. filter users down to $n$ users with following thresholds
		- at least x (25) submissions or comments in subreddits
		- submissions to at least 5 different subreddits in a given year
		- to exclude bots
			- need to make comments not only submissions
			- not belong to list of known reddit bots
			- not having string 'bot'
			- posted in more than 50 subreddits each month
	2. create matrix for directed graph $n \times n$-matrix
	- estimated time: 3 weeks for all reply networks
	- speculation:
		- growing number of edges as conspiracy theories gain more popularity? (in [[@monti2023]] varying number of edges; in- and de-creasing)
		- around 5 to 6 (only r/conspiracy) or even 7 digit number of nodes
4. (optional) compute other socio-demographic dimensions on community level
	1. integrate dimensions from previous project (without necessarily using them, but to get insight)
	2. compute education status dimension
	3. integrate activity level $\sum_s N_{u,s}$ for every user 
5. compute socio-demographic scores for users (for each user)
	1. get $N_{u,s}$ number of submissions of user $u$ in subreddit $s$
	2. simultaneously add all subreddit of this user to subreddit list $S$
	3. when finished with all users, save all community scores
	- maybe more performant:
		- first of two matrices with all (4-7) community scores of all subreddits where at least one user posted one submission in whole time span (for all years): $|dim| \times |s|$
		- second of two matrices with number of submissions $|s| \times |u|$-matrix
		- compute by multiplying and row sums
	- minimally: 4 dimensions from [[@waller2021]] (age, gender, affluence, political leaning)
	- standard: minimally + 2 dimensions (education, social)
	- extended: 
		- including user meta data (activity level, variety of subreddits)
			- for social dimension **or**
			- validation
		- validation with external/content data
			- NLP: language use, variety of words, length of posts, emotional tone
			- validation of *education* (indicated through language use and self-disclosures of profession)
			- validation of *online social interaction* (comparison with social dimension from previous project and meta data)
6. identify clusters within reply network
	- ==todo==: comparing methods (louvain, girwan newman, other)
	1. apply louvain method (modularity optimization) for clustering only on network structure
	- detect socio-demographic classes within or simultaneously to clusters? ==todo==
7. find classes of certain socio-demographic characteristic attributes/ look at distribution of socio-demographic scores for each cluster
	- compute means and optionally distributions of socio demographic scores within clusters
	- to get a first impression on influence of status homophily
8. create SD-model
	- ==todo== workload and what should be actually modified for our use-case?
9. create SD+t-model
	1. classify content with named entity recognition ==todo==


| Steps                                               | Planned Time period |
| ---------------------------------------------------------------------- | ------------------- |
| 1. collect r/Conspiracy comment data                                   | 1/2 to 1 week       |
| 2. extract reply network                                               | 1-2 weeks           |
| 3. compute socio-demographic scores for users                          | 1-2 weeks           |
| 4. identify clusters within reply network                              | 1 week              |
| 5. view socio-demographic mean and distribution clusters               | 1 week              |
| 6. logistic regression of interaction and SE characteristic attributes | 2-3 weeks           |
| 7. Generate SD model                                                   |  ==todo==                   |
| 8. Classify content                                                    |          ==todo==           |
| 8. Generate SD+t model                                                 |         ==todo==            |
| 7. Writing                                                             | 4 weeks             |
| 8. Correcting                                                          | 1 week              |

- **17 weeks in total since formal registration** (inclusing 1-2 weeks of christimas holidays)

# Notes

### Extra extra extra
- temporal dimension
- test-retest pre-post covid
- cross-country


### reddit data
- 56 subreddits [[@phadke2021]]
	- variance in degree of more similar to *r/science* or *r/conspiracy*
	- fallback: *r/conspiracy* (currently 2 mio members)
- timeline
	- proposal: 2018 - 2022
		- would capture the timeline over covid and growth of conspiracy theories
		- also 5 year span like in [[@monti2023]] about r/news
	- dependend on availability
- sources and availability 
	- convokit: data via subreddit
		- https://convokit.cornell.edu/documentation/subreddit.html
		- from inception till october 2018 (but uploaded october 2019)
			- speaker-level
			- utterance-level
			- conversation-level
			- corpus-level (list of subreddits and other number)
		- correctness
			- completeness not guaranteed (beta version release)
			- reply-to ID may not match any utterance that exists
	- ~~praw (python package?)~~
	- pushift datasets from 2005-6 till 2023-03 from [reddit post](https://www.reddit.com/r/pushshift/comments/146r0dx/historical_data_torrents_all_in_one_place/)`
		- in zst-files
		- from 2005-06 to 2022-12 via [academic torrents](https://academictorrents.com/details/7c0645c94321311bb05bd879ddee4d0eba08aaee)
		- "**License:** No license specified, the work may be protected by copyright."

### socio-demographic dimensions
- ideas
	- proposal: 
		- minimally: **age, gender, affluence, political leaning** from [[@waller2021]]
		- extra:
			- test how do the three social dimensions from previous project affect
			- extraction of 
				1. **academic education** level (construct on waller & anderson)
					- similar to previous project but reduction to academic status (sociologically: institutionalized cultural capital)
					- validation
						- maybe not so necessary because we orient on subreddits on elite universities and subreddits of academic disourse
						- otherwise: similar NLP validation as in previous project (for cultural capital) 
				2. social interaction
					- from previous project
					- otherwise: only online social capital
						- whether the person is in subreddits about being lonely, social anxiety etc. (oriented on previous project)
						- oriented on activity and interaction level of user
							- volume of postings, variety in subreddits
							- maybe: interaction (in form of amount of upvotes and comments)
				
	- on the one hand: socio-demographic dimensions on which conspiracy theorists are often labelled
		- age?
		- gender=male, education=low, political leaning=conservative, affluence=low, family status=unmarried, social interaction=weak/low, ethnicity= member of ethnic minority group [[@freeman2017]]
			- plus: education, family status (self-disclosures?), social interaction
	- on the other hand: which socio-demographic attributes in terms of status-homophily?
		- explorative
		- strong status homophily in age and affluence in *r/news* [[@monti2023]] (from 4 dimensions: age, gender, affluence, political leaning of [[@waller2021]])
	- availability: 
		-  4 dimensions [[@waller2021]]: age, gender, affluence, political leaning 
		- maybe from previous project: cultural and social capital
- **optional**: other non-socio-demographic dimensions from meta-data
	- activity-level of individual user
	- social reddit capital: how much do people upvote/comment on the user's submissions
