An Interactive Method for Inferring Demographic Attributes in Twitter

Beretta, Valentina; Maccagnola, Daniele; Cribbin, Timothy; Messina, Enza

doi:10.1145/2700171.2791031

Cited by 19 publications

(12 citation statements)

References 16 publications

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“…Dredze et al [ 35 ] state that geo-specific data coupled with the public forum nature of social media (which encourages the sharing of detailed information) creates new public health capabilities not previously seen. Simultaneously, advances in demographic extraction techniques and computational linguistics have allowed for a deeper understanding of user demographics [ 37 , 38 ]. In these studies, Beretta and Burger connected age and gender to linguistic patterns (often word usage).…”

Section: Introductionmentioning

confidence: 99%

Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter

Li¹,

Markson²,

Costello³

et al. 2016

JMIR Public Health Surveill

117

109

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BackgroundAs social media becomes increasingly popular online venues for engaging in communication about public health issues, it is important to understand how users promote knowledge and awareness about specific topics.ObjectiveThe aim of this study is to examine the frequency of discussion and differences by race and ethnicity of cancer-related topics among unique users via Twitter.MethodsTweets were collected from April 1, 2014 through January 21, 2015 using the Twitter public streaming Application Programming Interface (API) to collect 1% of public tweets. Twitter users were classified into racial and ethnic groups using a new text mining approach applied to English-only tweets. Each ethnic group was then analyzed for frequency in cancer-related terms within user timelines, investigated for changes over time and across groups, and measured for statistical significance.ResultsObservable usage patterns of the terms "cancer", "breast cancer", "prostate cancer", and "lung cancer" between Caucasian and African American groups were evident across the study period. We observed some variation in the frequency of term usage during months known to be labeled as cancer awareness months, particularly September, October, and November. Interestingly, we found that of the terms studied, "colorectal cancer" received the least Twitter attention.ConclusionsThe findings of the study provide evidence that social media can serve as a very powerful and important tool in implementing and disseminating critical prevention, screening, and treatment messages to the community in real-time. The study also introduced and tested a new methodology of identifying race and ethnicity among users of the social media. Study findings highlight the potential benefits of social media as a tool in reducing racial and ethnic disparities.

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Section: Introductionmentioning

confidence: 99%

Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter

Li¹,

Markson²,

Costello³

et al. 2016

JMIR Public Health Surveill

117

109

View full text Add to dashboard Cite

show abstract

“…As we discussed earlier, textual information is rich in content. It can leak users' privacy by allowing users' in the textual database to be re-identified [50] and leaking their private attribute information [15,43]. Our focus in this paper is to design an effective text anonymiztion technique for the data publisher to preserve users' privacy by preventing a potential adversary (i.e., malicious data consumer) from breaching privacy of users while maintaining the utility of their textual information for future tasks.…”

Section: Problem Statementmentioning

confidence: 99%

“…Today starting counting calories #myfitnesspal and juicing for dinner 2 This user may not be aware that the sensitive medical condition information can be easily inferred from this post-exposing symptoms of Diabetes. If intact users' textual data is available, a malicious data consumer (or any potential adversary) can easily infer lots of sensitive and private information from text that users' do not explicitly disclose such as vacation plans, medical conditions, age and location [15,31]. Another privacy issue arises when a malicious data consumer attempts to re-identify the identity of an individual in the database by investigating whether a targeted user's textual data is in the database or inferring which record is associated with it.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Text Representation Learning

Beigi

Shu

Guo

et al. 2019

Proceedings of the 30th ACM Conference on Hypertext and Social Media

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Online users generate tremendous amounts of textual information by participating in different activities, such as writing reviews and sharing tweets. This textual data provides opportunities for researchers and business partners to study and understand individuals. However, this user-generated textual data not only can reveal the identity of the user but also may contain individual's private information (e.g., age, location, gender). Hence, "you are what you write" as the saying goes. Publishing the textual data thus compromises the privacy of individuals who provided it. The need arises for data publishers to protect people's privacy by anonymizing the data before publishing it. It is challenging to design effective anonymization techniques for textual information which minimizes the chances of re-identification and does not contain users' sensitive information (high privacy) while retaining the semantic meaning of the data for given tasks (high utility). In this paper, we study this problem and propose a novel double privacy preserving text representation learning framework, DPText, which learns a textual representation that (1) is differentially private, (2) does not contain private information and (3) retains high utility for the given task. Evaluating on two natural language processing tasks, i.e., sentiment analysis and part of speech tagging, we show the effectiveness of this approach in terms of preserving both privacy and utility.

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“…Many build learning models that are applicable across a variety of different user attributes (Chen et al, 2015a;Volkova et al, 2015;Beretta et al, 2015). Among the attributes, political preferences is a frequent area of research, again relying on features from user tweets, and making use of graph-based algorithms over their friends' attributes (Golbeck and Hansen, 2011;Conover et al, 2011;Wong et al, 2013;Cohen and Ruths, 2013;Volkova et al, 2014).…”

Section: Previous Workmentioning

confidence: 99%

Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media

Lin¹,

Ku²,

Li³

et al. 2017

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ii SocialNLP@EACL2017 Chairs' Welcome It is our great pleasure to welcome you to the Fifth International Workshop on Natural Language Processing for SocialMedia-SocialNLP 2017, associated with EACL 2017. SocialNLP is an interdisciplinary area of natural language processing (NLP) and social computing. We hold SocialNLP twice a year: one in the NLP venue, the other in the associated venue such as those for web technology or artificial intelligence. There are three plausible directions of SocialNLP: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social media; and (3) handling new problems related to both social computing and natural language processing. Through this workshop, we anticipate to provide a platform for research outcome presentation and head-to-head discussion in the area of SocialNLP, with the hope to combine the insight and experience of prominent researchers from both NLP and social computing domains to contribute to the area of SocialNLP jointly. The submissions to this year's workshop were again of high quality and we had a competitive selection process. We received 13 submissions from Asia, Europe, and the United States, and due to a rigorous review process, we only accepted 6 long oral papers. Thus the acceptance rate was 46 percent. Compared to the recent workshops in United States, the submission number is a bit small. This also encourages us to have more related activities in Europe to expand our community here.This year, we are delighted to have Prof. Dirk Hovy, from the University of Copenhagen, as our keynote speaker. We also encourage attendees to attend the keynote and invited talk presentation to have more discussions with outstanding researchers. Their valuable and insightful talk can and will guide us to a better understanding of the future. Putting together SocialNLP 2017 was a team effort. We first thank the authors for providing the quality content of the program. We are grateful to the program committee members, who worked very hard in reviewing papers and providing feedback for authors. Finally, we especially thank the Workshop Committee Chairs Prof. Laura Rimell and Prof. Richard Johansson.We hope you join our community and enjoy the workshop! OrganizersLun-Wei Ku, Academia Sincia, Taiwan Cheng-Te Li, National Cheng Kung University, Taiwan AbstractThis paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.

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An Interactive Method for Inferring Demographic Attributes in Twitter

Cited by 19 publications

References 16 publications

Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter

Leveraging Social Media to Promote Public Health Knowledge: Example of Cancer Awareness via Twitter

Privacy Preserving Text Representation Learning

Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media

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