Twitter: a content analysis of personal information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractSocial media provide many opportunities to connect people, but the kinds of personally identifiable information that people share through social media is understudied. This paper presents findings from a content analysis in which we coded the amount and kinds of personally identifiable information of public Twitter messages. Overwhelmingly, public Twitter messages do not include identifiable information such as phone numbers, email, and home addresses. Using Goffman's [(1963). Behaviors in public places: Notes on the social organization of gatherings. New York, NY: Free Press] concepts of public order and civil inattention, we also coded for whether people articulate the kinds of information that are communicated with others in public space, including locational, temporal, and activity-related information. Our findings suggest that people do share similar kinds of personal information on Twitter that they do in others kinds of physical public spaces, suggesting that people may also be mapping old practices for public social interaction onto networked publics.Keywords: social mediapublic orderprivacycivil inattentionTwitterlocation AcknowledgementsSpecial thanks go to Cornell's New Media and Society group for comments on an earlier draft of the paper and to the research assistants who worked on this project: Adam Agata, Ordessia Charron, Madalyn Darnell, Betsy Distelberger, Kim McErlean, Claudia Mei, Elizabeth Newbury, Adi Potashnic, and Deborah Tan.FundingThis work was supported by the USDA/NIFA [ID 0223492].Notes on ContributorsLee Humphreys, PhD is an assistant professor of Communication at Cornell University. [email: lmh13@cornell.edu]Phillipa Gill, PhD is an assistant professor of Computer Science at Stony Brook University. [email: Phillipa.Gill@stonybrook.edu]Balachander Krishnamurthy, PhD is a lead member of technical staff, research at AT&T Labs – Research. [email: bala@research.att.com]Notes1. User ID is different from names or usernames. We specifically wanted to not collect the names to maintain some privacy of the tweets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it