Forensic future of social media analysis using web ontology

Mohan, A. K.; Venkataraman, D.

doi:10.1109/icaccs.2017.8014682

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…This mainly focuses on the investigations of three core issues such a data volume, heterogeneous data, and the investigator's cognitive load in studying the relationships between artifacts. This framework is used to assess the possibility of using metadata and semantic web-based ontologies by Mohan, A. K., & Venkataraman, D. (2017, January) to solve the problems of big data and heterogeneous data sources correspondingly. The advantage of extending these two models to an ontology-based forensics analysis will help in a greater way for evidence examination.…”

Section: Discussionmentioning

confidence: 99%

Holistic Analytics of Digital Artifacts

Mohan

Sethumadhavan

Lakshmy

2021

International Journal of Digital Crime and Forensics

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Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the investigators. The effort of this article via unique pockets (UP), unique groups (UG), and unique association (UA) model is to address the exclusive challenges mixed up in identifying incoherent associations that are buried well within the meager metadata field-value pairs. Both the existing similarity models and proposed unique mapping models are verified by the unique metadata association model.

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Section: Discussionmentioning

confidence: 99%

Holistic Analytics of Digital Artifacts

Mohan

Sethumadhavan

Lakshmy

2021

International Journal of Digital Crime and Forensics

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“…But the pheme datasets are used a Twitter API, by using this API tweet crawlers cant able to scrape the previous years twitter data. But for the proposed investigation scenario, essentially we have to collect the older information from any social media [17]. To avoid the above limitation, tweets are collected and dataset is generated using the twitter scraper framework.…”

Section: Data Collectionmentioning

confidence: 99%

“…Comparatively, the internet users spend more time on social networks than the search engines. People started building social media relationships [17] to improve direct communication with other online users. This kind of technology growth can be used to deduce each individual's social relationship with others through online social media networks.…”

Section: Introductionmentioning

confidence: 99%

Isolating Rumors Using Sentiment Analysis

Sivasangari

Mohan

Suthendran

et al. 2018

JCSANDM

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In recent days, social media has become a platform to spread false facts all the way through internet. One of the growing data analytic engine from web informal organization says, twitter has become the prime source for spreading fake news facilitating numerous perpetrators around the globe. It has turned into a competent, speedy cum effortless hotspot for news-fans to just click and forward junk data. Individuals are opting to use twitter for searching information regarding crisis circumstances and everyday occasions. In twitter, the spread of fraudulent or inaccurate information during the emergency situations will affect the individuals and public in numerous ways, but the original news is more reliable when it is declared by the news channels. So, the importance of proposed framework is categorized in three steps; Initially, Twitter Scraper is applied to scrape the vast volume of tweets and metadata from the collected set of tweets for the study on former Chief Minister (state of Tamil Nadu) death case controversy during 2016. Then the threshold value based on negative polarity of common tweets for the scraped data is calculated, once the tweeted texts are different from the threshold condition it will be automatically tagged as ‘rumor’ (negative) or else tagged as ‘non-rumor’ (positive) using sentiment classifier. Finally, the proposed model on VADER based sentiment analysis identifies the false facts. It is obtained as a result of the sample tweets regularly training and testing it on whole datasets.

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