3,904 research outputs found
Customer Behavior Analysis for Social Media
It is essential for a business organization to get the customer feedback in order to grow as a company. Business organizations are collecting customer feedback using various methods. But the question is âare they efficient and effective?' In the current context, there is more of a customer oriented market and all the business organizations are competing to achieve customer delight through their products and services. Social Media plays a huge role in one's life. Customers tend to reveal their true opinion about certain brands on social media rather than giving routine feedback to the producers or sellers. Because of this reason, it is identified that social media can be used as a tool to analyze customer behavior. If relevant data can be gathered from the customers' social media feeds and if these data are analyzed properly, a clear idea to the companies what customers really think about their brand can be provided
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
Detecting and Monitoring Hate Speech in Twitter
Social Media are sensors in the real world that can be used to measure the pulse of societies.
However, the massive and unfiltered feed of messages posted in social media is a phenomenon that
nowadays raises social alarms, especially when these messages contain hate speech targeted to a
specific individual or group. In this context, governments and non-governmental organizations
(NGOs) are concerned about the possible negative impact that these messages can have on individuals
or on the society. In this paper, we present HaterNet, an intelligent system currently being used by
the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that
identifies and monitors the evolution of hate speech in Twitter. The contributions of this research
are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social
network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on
hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification
approaches based on different document representation strategies and text classification models. (4)
The best approach consists of a combination of a LTSM+MLP neural network that takes as input the
tweetâs word, emoji, and expression tokensâ embeddings enriched by the tf-idf, and obtains an area
under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the
literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation
grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Unionâs Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge
A systematic literature review on spam content detection and classification
The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e ., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection
MISNIS: an intelligent platform for Twitter topic mining
Twitter has become a major tool for spreading news, for dissemination of positions and ideas, and for the commenting and analysis of current world events. However, with more than 500 million tweets flowing per day, it is necessary to find efficient ways of collecting, storing, managing, mining and visualizing all this information. This is especially relevant if one considers that Twitter has no ways of indexing tweet contents, and that the only available categorization âmechanismâ is the #hashtag, which is totally dependent of a user's will to use it. This paper presents an intelligent platform and framework, named MISNIS - Intelligent Mining of Public Social Networksâ Influence in Society - that facilitates these issues and allows a non-technical user to easily mine a given topic from a very large tweet's corpus and obtain relevant contents and indicators such as user influence or sentiment analysis.
When compared to other existent similar platforms, MISNIS is an expert system that includes specifically developed intelligent techniques that: (1) Circumvent the Twitter API restrictions that limit access to 1% of all flowing tweets. The platform has been able to collect more than 80% of all flowing portuguese language tweets in Portugal when online; (2) Intelligently retrieve most tweets related to a given topic even when the tweets do not contain the topic #hashtag or user indicated keywords. A 40% increase in the number of retrieved relevant tweets has been reported in real world case studies.
The platform is currently focused on Portuguese language tweets posted in Portugal. However, most developed technologies are language independent (e.g. intelligent retrieval, sentiment analysis, etc.), and technically MISNIS can be easily expanded to cover other languages and locations
Whatâs Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter
© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter
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