How publishing works in this field
Journals here are strongly method-identified. A critical-theoretical paper sent to a computational journal, or a large-n content analysis sent to a cultural studies journal, is usually desk-rejected on fit rather than quality. Reading the last two issues tells you more than the aims and scope statement does.
Platform data has become the field's defining constraint. API access has narrowed sharply, terms of service limit redistribution, and a dataset collected under one set of rules may not be shareable under another. Papers now need to state how data were collected, under what terms, and what can be released.
What slows papers down here
Reproducibility without redistributable data is the live problem. Where raw platform data cannot be shared, reviewers expect the collection code, the query parameters, the date range and enough detail for someone else to repeat the collection — not an assurance that it was done carefully.
Ethics review for public but identifiable content catches authors out. Posts are public; the people who wrote them did not consent to being quoted in research. Journals increasingly expect a statement on how identifiability was handled, including whether quotations are searchable back to an account.
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Questions we are asked in this field
Often yes, but the ethics are not settled by the data being public. Journals expect a reasoned position on identifiability — whether quotes are searchable, whether accounts are named, whether vulnerable groups are involved — and an ethics committee view where your institution requires one.
No, but it changes the statement. Name the platform terms that prevent sharing, and release what you can: collection scripts, parameters, date ranges, aggregate counts and a codebook. That is what reviewers are actually asking for.
Read recent issues rather than the scope statement, and look at who is cited. If your methods section would look foreign in the journal's last ten papers, the fit is wrong regardless of topic overlap.