KOMPARASI KEJAHATAN DI TWITTER DAN INSTAGRAM DENGAN PENDEKATAN DIGITAL FORENSIC INVESTIGATION

Semakin meningkat perkembangan teknologi dalam kehidupan sehari-hari, maka semakin meningka pula tindak kejahatan dalam sosial media. Salah satu media sosial yang sering menjadi sasaran kejahatan ialah twitter dan instagram. Adapun digital forensic investigation merupakan bagian ilmu forensic yang melingkupi penemuan dan investigasi data yang ditemukan pada perangkat digital atau juga dikenal sebagai Ilmu Forensik Digital, yang mana merupakan salah satu cabang ilmu forensic yang berfokus pada penyelidikan dan penemuan konten perangkat digital dan seringkali dikaitkan dengan kejahatan komputer. Dengan begitu melalui pendekatan digital forensic investigation terhadap twitter dan instagram diharapkan dapat menjadi tolak ukur bahaya terkait komparasi kejahatan pada twitter dan instagram. Metode yang akan digunakan yaitu National Institute of Justice (NIJ) dengan tahapan berikut Collection, Examination, Analysis dan Reporting. Metode berikut diharapkan dapat menghasilkan bukti digital forensik yang dapat mengetahui komparasi kejahatan pada media sosial twitter dan instagram

Benford's Law Applies To Online Social Networks

Benford's Law states that the frequency of first digits of numbers in naturally occurring systems is not evenly distributed. Numbers beginning with a 1 occur roughly 30\% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. We consider social data from five major social networks: Facebook, Twitter, Google Plus, Pinterest, and Live Journal. We show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same holds for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follow the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.Comment: 9 pages, 2 figure

Spartan Daily, October 5, 2016

Volume 147, Issue 15https://scholarworks.sjsu.edu/spartan_daily_2016/1055/thumbnail.jp

PRNU-based image classification of origin social network with CNN

A huge amount of images are continuously shared on social networks (SNs) daily and, in most of cases, it is very difficult to reliably establish the SN of provenance of an image when it is recovered from a hard disk, a SD card or a smartphone memory. During an investigation, it could be crucial to be able to distinguish images coming directly from a photo-camera with respect to those downloaded from a social network and possibly, in this last circumstance, determining which is the SN among a defined group. It is well known that each SN leaves peculiar traces on each content during the upload-download process; such traces can be exploited to make image classification. In this work, the idea is to use the PRNU, embedded in every acquired images, as the “carrier” of the particular SN traces which diversely modulate the PRNU. We demonstrate, in this paper, that SN-modulated noise residual can be adopted as a feature to detect the social network of origin by means of a trained convolutional neural network (CNN)

The Ethics of Troubled Images

This special issue of Cultural Studies Review brings together an interdisciplinary range of scholarship to investigate the ethical implications of troubled images

The Remanence of Medieval Media

The Remanence of Medieval Media (uncorrected, pre-publication version) For: The Routledge Handbook of Digital Medieval Literature, edited by Jen Boyle and Helen Burgess (2017

Do President Trump’s Tweets Increase Uncertainty in the US Economy?

When President Trump tweets, does it change uncertainty in the economy? This study gathers President Trump’s tweets off his twitter accounts from October 2016 to October 2017 and classifies each of them as either negative (expected to increase uncertainty), positive (expected to decrease uncertainty) or neutral (no effect expected on uncertainty). I find that tweets with a negative sentiment were followed by an increase in uncertainty in the VIX and S&P 500 1 and 2 minutes after the tweet. Similar results were found for positive tweets. Non-neutral tweets increase trading volume in the VIX and S&P 500 by up to 200% in the hour following the tweet. Overall, this study finds that up to 2 minutes after the non-neutral tweet, investors appear to be trading based on the sentiment value of that tweet. However, after 2 minutes, investors trading strategies appear to veer away from tweet sentiment value, but trading volumes in these indices are still much higher than normal. Policy makers should be aware of this increased volatility stemming from the President’s unconventional twitter use and may suggest more conventional ways to spread new information to the financial markets

Pre processing of social media remarks for forensics

The Internet's rapid growth has led to a surge in social network users, resulting in an increase in extreme emotional and hate speech online. This study focuses on the security of public opinion in cyber security by analyzing Twitter data. The goal is to develop a model that can detect both sentiment and hate speech in user texts, aiding in the identification of content that may violate laws and regulations. The study involves pre processing the acquired forensic data, including tasks like lowercasing, stop word removal, and stemming, to obtain clear and effective data. This paper contributes to the field of public opinion security by linking forensic data with machine learning techniques, showcasing the potential for detecting and analyzing Twitter text data