Deep Dive into Anonymity: Large Scale Analysis of Quora Questions

Anonymity forms an integral and important part of our digital life. It enables us to express our true selves without the fear of judgment. In this paper, we investigate the different aspects of anonymity in the social Q&A site Quora. Quora allows users to explicitly post anonymous questions and such activity in this forum has become normative rather than a taboo. Through an analysis of millions of questions, we observe that at a global scale almost no difference manifests between the linguistic structure of the anonymous and the non-anonymous questions posted on Quora. We find that topical mixing at the global scale to be the primary reason for the absence. However, the differences start to feature once we “deep dive” and (topically) cluster the questions and compare them. In particular, we observe that the choice to post the question as anonymous is dependent on the user’s perception of anonymity and they often choose to speak about depression, anxiety, social ties and personal issues under the guise of anonymity. Subsequently, to gain further insights, we build an anonymity grid to identify the differences in the perception on anonymity of the user posting the question and the community of users answering it. We also look into the first response time of the questions and observe that it is lowest for topics which talk about personal and sensitive issues, which hints toward a higher degree of community support.

1. 1.

   www.quora.com/Why-did-Quora-add-the-anonymous-option-for-answers-and-questions.

2. 2.

   We did not remove the users whose accounts were deleted. On deletion, the username is simply replaced with “user” placeholder and does not make it anonymous. For more details please visit: https://www.quora.com/What-happens-when-I-deactivate-or-delete-my-Quora-account.

3. 3.

   https://goo.gl/EFXQJx.

4. 4.

   We use the POS (Parts of Speech) tagger and NER (Named Entity Recognizer) of SpaCy: https://spacy.io.

5. 5.

   We use VADER [8].

6. 6.

   http://liwc.wpengine.com/.

7. 7.

   Definition available in Appendix.

8. 8.

   http://universaldependencies.org/u/pos/.

Authors and Affiliations

1. Indian Institute of Technology Kharagpur, Kharagpur, India

   Binny Mathew, Ritam Dutt, Pawan Goyal & Animesh Mukherjee

2. Northwestern University, Evanston, USA

   Suman Kalyan Maity

Corresponding author

Correspondence to Binny Mathew .

Editors and Affiliations

1. Qatar Computing Research Institute, Doha, Qatar

   Ingmar Weber

2. Qatar Computing Research Institute, Doha, Qatar

   Kareem M. Darwish

3. University of Koblenz-Landau, Koblenz, Germany

   Claudia Wagner

4. Max Planck Institute for Demographic Research, Rostock, Germany

   Emilio Zagheni

5. Northeastern University, Boston, MA, USA

   Laura Nelson

6. Max Planck Institute for Demographic Research, Rostock, Germany

   Samin Aref

7. GESIS-Leibniz Institute for the Social Sciences, Cologne, Germany

   Fabian Flöck

A Appendix

Cohen’s d: The Cohen’s d test is primarily used to measure the effect size, which is an estimate of the strength of the relationship of two variables. Given the mean and standard deviation of two populations, denoted by \(\mu _{1}\) and \(\sigma _{1}\) and \(\mu _{2}\) and \(\sigma _{2}\) respectively, the Cohen’s d-test is the ratio of the difference of their means to their pooled standard deviation, more succinctly represented by the following expression

A Cohen’s d-value of magnitude 0.2 indicates small effect, 0.5 indicates medium effect while 0.8 signifies large effect.

Diff(%): We use the metric \(Diff(\%)\) in a way similar to Correa et al. [4] to quantify the difference in features between the Anon and the Known Group. The Diff(%) metric is simply the percentage mean difference of a feature between the Anon and Known group and is represented as

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