Identifying offenders on Twitter: A law enforcement practitioner guide

Abstract Twitter remains one of the most popular social media network sites in use today and continues to attract criticism over the volume of unsavoury and illegal content circulated by its users. When breaches of legislation occur, appropriate officials are left with the task of identifying and apprehending the physical user of an offending account, which is not always a simple task. This article provides a law enforcement practitioner focused analysis of the Twitter platform and associated services for the purposes of offender identification. Using our bespoke message harvesting tool ‘Twitterstream’, an analysis of the data available via Twitter's Streaming and REST APIs are presented, along with the message metadata which can be gleaned. The process of identifying those behind offending Twitter accounts is discussed in line with available API content and current Twitter data retention policies in order to support law enforcement investigations surrounding this social media platform.

[1]  Clare Llewellyn,et al.  Brexit? Analyzing Opinion on the UK-EU Referendum within Twitter , 2016, ICWSM.

[2]  Laura Whitney Lee,et al.  Silencing the "Twittering Juror": The Need to Modernize Pattern Cautionary Jury Instructions to Reflect the Realities of the Electronic Age , 2014 .

[3]  Adam Pendlebury,et al.  Tweeting the game into disrepute: regulation of social media by governing bodies - lessons from English football , 2015 .

[4]  Dhiraj Murthy,et al.  Visual Social Media and Big Data. Interpreting Instagram Images Posted on Twitter , 2016 .

[5]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[6]  Tao Cheng,et al.  Event Detection using Twitter: A Spatio-Temporal Approach , 2014, PloS one.

[7]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[8]  Ning Wang,et al.  Assessing the bias in samples of large online networks , 2014, Soc. Networks.

[9]  Graeme Horsman A survey of current social network and online communication provision policies to support law enforcement identify offenders , 2017, Digit. Investig..

[10]  L. Jaba Sheela,et al.  A Review of Sentiment Analysis in Twitter Data Using Hadoop , 2016 .

[11]  Omer F. Rana,et al.  Can We Predict a Riot? Disruptive Event Detection Using Twitter , 2017, ACM Trans. Internet Techn..

[12]  Timothy W. Finin,et al.  CyberTwitter: Using Twitter to generate alerts for cybersecurity threats and vulnerabilities , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[13]  Huan Liu,et al.  Twitter Data Analytics , 2013, SpringerBriefs in Computer Science.

[14]  Peter Coe Footballers and social media ‘Faux Pas’:the Football Association’s cash cow? , 2015 .

[15]  Adeline A. Allen Twibel Retweeted: Twitter Libel and the Single Publication Rule , 2014 .

[16]  Avi Arampatzis,et al.  Stock Price Forecasting via Sentiment Analysis on Twitter , 2016, PCI.

[17]  Alisdair A. Gillespie Regulation of internet surveillance. , 2009 .

[18]  Jitendra Kumar Rout,et al.  Malicious Account Detection Based on Short URLs in Twitter , 2017 .

[19]  Rui Li,et al.  TEDAS: A Twitter-based Event Detection and Analysis System , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[20]  Anupam Joshi,et al.  Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy , 2013, WWW.

[21]  Danah Boyd,et al.  Social network sites: definition, history, and scholarship , 2007, IEEE Engineering Management Review.

[22]  Kasturi Dewi Varathan,et al.  Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network , 2016, Comput. Hum. Behav..