Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones

@article{Zhou2016TweetPA,
  title={Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones},
  author={Lu Zhou and Wenbo Wang and Keke Chen},
  journal={Proceedings of the 25th International Conference on World Wide Web},
  year={2016}
}
  • Lu Zhou, Wenbo Wang, Keke Chen
  • Published 11 April 2016
  • Computer Science
  • Proceedings of the 25th International Conference on World Wide Web
Inappropriate tweets can cause severe damages on authors' reputation or privacy. However, many users do not realize the negative consequences until they publish these tweets. Published tweets have lasting effects that may not be eliminated by simple deletion because other users may have read them or third-party tweet analysis platforms have cached them. Regrettable tweets, i.e., tweets with identifiable regrettable contents, cause the most damage on their authors because other users can easily… 
Manipulating Twitter Through Deletions
TLDR
This is the first large-scale analysis of anomalous deletion patterns involving more than a billion deletions by over 11 million ac- counts and provides platforms and researchers with new meth- ods for identifying social media abuse.
Classification of Private Tweets Using Tweet Content
TLDR
This paper makes the first attempt to classify private tweets into 14 categories, such as alcohol & drugs, family information, etc, and model tweet semantic with term distribution features as well as users' topic-preferences based on personal tweet history.
Prediction of Twitter Message Deletion
  • A. Gazizullina, M. Mazzara
  • Computer Science
    2019 12th International Conference on Developments in eSystems Engineering (DeSE)
  • 2019
TLDR
This paper analyzes Twitter messages in English language with the objective to build a classifier to predict whether a particular post will be deleted by the user or not, and applies the Recurrent Neural Networks (RNN) model that relies on the context-based information of tweets while doing the classification.
Forgetting in Social Media: Understanding and Controlling Longitudinal Exposure of Socially Shared Data
TLDR
This study finds that a significant fraction of users withdraw a surprisingly large percentage of old publicly shared data—more than 28% of six-year old public posts (tweets) on Twitter are not accessible today.
A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election
TLDR
The aim of this study is to highlight the importance of conducting sentiment analysis on all posts captured in real time, including those that are now inaccessible, in determining the true sentiments of the opinions around the time of an event.
Managing longitudinal exposure of socially shared data on the Twitter social media
TLDR
This study finds that a significant fraction of users withdraw a surprisingly large percentage of old publicly shared data—more than 28% of 6-year old public posts (tweets) on Twitter are not accessible today.
Deceptive Deletions for Protecting Withdrawn Posts on Social Platforms
TLDR
The Deceptive Deletion mechanism is applied to a real-world task on Twitter: hiding damaging tweet deletions, showing that a powerful global adversary can be beaten by a powerful challenger, raising the bar significantly and giving a glimmer of hope in the ability to be really forgotten on social platforms.
Deceptive Deletions for Protecting Withdrawn Posts on Social Media Platforms
TLDR
The Deceptive Deletion mechanism is applied to a real-world task on Twitter: hiding damaging tweet deletions, showing that a powerful global adversary can be beaten by a powerful challenger, raising the bar significantly and giving a glimmer of hope in the ability to be really forgotten on social platforms.
BlackLivesMatter 2020: An Analysis of Deleted and Suspended Users in Twitter
TLDR
This study analyzes what happened in Twitter before and after the event triggers with respect to deleted and suspended users to find that the users who participated to the 2020 BlackLivesMatter discussion have more negative and undesirable tweets, compared to the Users who did not.
Empirical Understanding of Deletion Privacy: Experiences, Expectations, and Measures
TLDR
A user survey-based exploration involving 191 participants to unpack their prior deletion experiences, their expectations of deletion privacy, and how effective they think the current deletion mechanisms are.
...
...

References

SHOWING 1-10 OF 36 REFERENCES
Tweets are forever: a large-scale quantitative analysis of deleted tweets
TLDR
Some significant differences were discovered in the clients used to post them, their conversational aspects, the sentiment vocabulary present in them, and the days of the week they were posted, but in other dimensions for which analysis was possible, no substantial differences were found.
Study of Trend-Stuffing on Twitter through Text Classification
TLDR
This work studies the use of text-classification over 600 trends consisting of 1.3 million tweets and their associated web pages to identify tweets that are closely-related to a trend as well as unrelated tweets.
Suspended accounts in retrospect: an analysis of twitter spam
TLDR
This study examines the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon and identifies an emerging marketplace of illegitimate programs operated by spammers.
Self-disclosure topic model for classifying and analyzing Twitter conversations
TLDR
The developed self-disclosure topic model (SDTM) significantly outperforms several comparable methods on classifying the level of selfdisclosure, and the analysis of the longitudinal data using SDTM uncovers significant and positive correlation between self Disclosure and conversation frequency and length.
I Wish I Didn't Say That! Analyzing and Predicting Deleted Messages in Twitter
TLDR
It is shown how deletions can be automatically predicted ahead of time and analysed which tweets are likely to be deleted and how.
"i read my Twitter the next morning and was astonished": a conversational perspective on Twitter regrets
TLDR
It was found that participants who posted on Twitter became aware of, and tried to repair, regret more slowly than those reporting in-person regrets.
Time is of the essence: improving recency ranking using Twitter data
TLDR
A method to use the micro-blogging data stream to detect fresh URLs and to compute novel and effective features for ranking fresh URLs is proposed and demonstrated to improve effective of the portal web search engine for realtime web search.
"I regretted the minute I pressed share": a qualitative study of regrets on Facebook
We investigate regrets associated with users' posts on a popular social networking site. Our findings are based on a series of interviews, user diaries, and online surveys involving 569 American
Cursing in English on twitter
TLDR
This paper examines the characteristics of cursing activity on a popular social media platform - Twitter - involving the analysis of about 51 million tweets and about 14 million users to explore a set of questions that have been recognized as crucial for understanding cursing in offline communications.
Mining Privacy Settings to Find Optimal Privacy-Utility Tradeoffs for Social Network Services
  • Shumin Guo, Keke Chen
  • Computer Science
    2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing
  • 2012
TLDR
A tradeoff algorithm is developed for helping users find the optimal privacy settings for a specified level of privacy concern and a personalized utility preference and a framework for users to conveniently tune the privacy settings towards the user desired privacy level and social utilities is proposed.
...
...