Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

An Analysis of the Consequences of the General Data Protection Regulation on Social Network Research

This article examines the principles outlined in the General Data Protection Regulation in the context of social network data. We provide both a practical guide to General Data Protection Regulation--compliant social network data processing, covering aspects such as data collection, consent, anonymization, and data analysis, and a broader discussion of the problems emerging when the general principles on which the regulation is based are instantiated for this research area

Social Bots: Human-Like by Means of Human Control?

Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term `Social Bot' is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure

TSTEM: A Cognitive Platform for Collecting Cyber Threat Intelligence in the Wild

The extraction of cyber threat intelligence (CTI) from open sources is a rapidly expanding defensive strategy that enhances the resilience of both Information Technology (IT) and Operational Technology (OT) environments against large-scale cyber-attacks. While previous research has focused on improving individual components of the extraction process, the community lacks open-source platforms for deploying streaming CTI data pipelines in the wild. To address this gap, the study describes the implementation of an efficient and well-performing platform capable of processing compute-intensive data pipelines based on the cloud computing paradigm for real-time detection, collecting, and sharing CTI from different online sources. We developed a prototype platform (TSTEM), a containerized microservice architecture that uses Tweepy, Scrapy, Terraform, ELK, Kafka, and MLOps to autonomously search, extract, and index IOCs in the wild. Moreover, the provisioning, monitoring, and management of the TSTEM platform are achieved through infrastructure as a code (IaC). Custom focus crawlers collect web content, which is then processed by a first-level classifier to identify potential indicators of compromise (IOCs). If deemed relevant, the content advances to a second level of extraction for further examination. Throughout this process, state-of-the-art NLP models are utilized for classification and entity extraction, enhancing the overall IOC extraction methodology. Our experimental results indicate that these models exhibit high accuracy (exceeding 98%) in the classification and extraction tasks, achieving this performance within a time frame of less than a minute. The effectiveness of our system can be attributed to a finely-tuned IOC extraction method that operates at multiple stages, ensuring precise identification of relevant information with low false positives