This document discusses methods for detecting spammers and fake users on social networks like Twitter. It provides a literature review of past research on spam detection techniques on Twitter. The techniques are categorized based on their ability to detect: (1) fake content, (2) spam based on URLs, (3) spam in popular topics, and (4) fake users. The techniques are also analyzed based on attributes like user attributes, content attributes, graph attributes, structural attributes, and time attributes. An implementation approach is proposed that involves collecting user data, employing machine learning algorithms like random forest for pattern recognition, using feature engineering, and integrating social media APIs for real-time monitoring. It also discusses integrating a user reporting mechanism to improve accuracy
Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Se...ijtsrd
Online Social Networks OSNs are providing a diversity of application for human users to network through families, friends and even strangers. One of such application, friend search engine, allows the universal public to inquiry individual client friend lists and has been gaining popularity recently. Proper design, this application may incorrectly disclose client private relationship information. Existing work has a privacy perpetuation clarification that can effectively boost OSNs' sociability while protecting users' friendship privacy against attacks launched by individual malicious requestors. In this project proposed an advanced collusion attack, where a victim user's friendship privacy can be compromise from side to side a series of cautiously designed queries coordinately launched by multiple malicious requestors. The result of the proposed collusion attack is validate through synthetic and real world social network data sets. The project on the advanced collusion attacks will help us design a more vigorous and securer friend search engine on OSNs in the near future. R. Brintha | H. Parveen Bagum "Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Search Engine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31687.pdf Paper Url :https://www.ijtsrd.com/computer-science/world-wide-web/31687/retrieving-hidden-friends-a-collusion-privacy-attack-against-online-friend-search-engine/r-brintha
In the age of social media communication, it is easy to
modulate the minds of users and also instigate violent
actions being taken by them in some cases. There is a need
to have a system that can analyze the threat level of tweets
from influential users and rank their Twitter handles so
that dangerous tweets can be avoided going public on
Twitter before fact-checking which can hurt the sentiments
of people and can take the shape of violence. The study
aims to analyse and rank twitter users according to their
influential power and extremism of their tweets to help
prevent major protests and violent events. We scraped top
trending topics and fetched tweets using those hashtags.
We propose a custom ranking algorithm which considers
source based and content based features along with a
knowledge graph which generates the score and rank the
twitter users according to the scores. Our aim with this
study is to identify and rank extremist twitter users with
regards to their impact and influence. We use a technique
that takes into consideration both source based and
content-based features of tweets to generate the ranking of
the extremist twitter users having a high impact factor
FRAMEWORK FOR ANALYZING TWITTER TO DETECT COMMUNITY SUSPICIOUS CRIME ACTIVITYcscpconf
This research work discusses how an integrated open source intelligence framework can help the law enforcements and government entities who are investigating crimes based on statistical and graph analysis on Twitter data. The solution supports a real-time and off-line analysis of the tweets collections. The framework employs tools that support big data processing capabilities, to collect, process and analyze a huge amount of data. The outline solution supports content and textual based analysis, helping the investigators to dig into a person and the community linked to that person based on a tweet. Our solution supports an investigative processes composed of the following phases (i) find suspicious tweets and individuals based on hash tags analysis (ii) classify the user profile based on Twitter features (iii) identify influencers in the FOAF networks of the senders (iiii) analyze these influencers’ background and history to find hints of past or current criminal activity.
Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Se...ijtsrd
Online Social Networks OSNs are providing a diversity of application for human users to network through families, friends and even strangers. One of such application, friend search engine, allows the universal public to inquiry individual client friend lists and has been gaining popularity recently. Proper design, this application may incorrectly disclose client private relationship information. Existing work has a privacy perpetuation clarification that can effectively boost OSNs' sociability while protecting users' friendship privacy against attacks launched by individual malicious requestors. In this project proposed an advanced collusion attack, where a victim user's friendship privacy can be compromise from side to side a series of cautiously designed queries coordinately launched by multiple malicious requestors. The result of the proposed collusion attack is validate through synthetic and real world social network data sets. The project on the advanced collusion attacks will help us design a more vigorous and securer friend search engine on OSNs in the near future. R. Brintha | H. Parveen Bagum "Retrieving Hidden Friends a Collusion Privacy Attack against Online Friend Search Engine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31687.pdf Paper Url :https://www.ijtsrd.com/computer-science/world-wide-web/31687/retrieving-hidden-friends-a-collusion-privacy-attack-against-online-friend-search-engine/r-brintha
In the age of social media communication, it is easy to
modulate the minds of users and also instigate violent
actions being taken by them in some cases. There is a need
to have a system that can analyze the threat level of tweets
from influential users and rank their Twitter handles so
that dangerous tweets can be avoided going public on
Twitter before fact-checking which can hurt the sentiments
of people and can take the shape of violence. The study
aims to analyse and rank twitter users according to their
influential power and extremism of their tweets to help
prevent major protests and violent events. We scraped top
trending topics and fetched tweets using those hashtags.
We propose a custom ranking algorithm which considers
source based and content based features along with a
knowledge graph which generates the score and rank the
twitter users according to the scores. Our aim with this
study is to identify and rank extremist twitter users with
regards to their impact and influence. We use a technique
that takes into consideration both source based and
content-based features of tweets to generate the ranking of
the extremist twitter users having a high impact factor
FRAMEWORK FOR ANALYZING TWITTER TO DETECT COMMUNITY SUSPICIOUS CRIME ACTIVITYcscpconf
This research work discusses how an integrated open source intelligence framework can help the law enforcements and government entities who are investigating crimes based on statistical and graph analysis on Twitter data. The solution supports a real-time and off-line analysis of the tweets collections. The framework employs tools that support big data processing capabilities, to collect, process and analyze a huge amount of data. The outline solution supports content and textual based analysis, helping the investigators to dig into a person and the community linked to that person based on a tweet. Our solution supports an investigative processes composed of the following phases (i) find suspicious tweets and individuals based on hash tags analysis (ii) classify the user profile based on Twitter features (iii) identify influencers in the FOAF networks of the senders (iiii) analyze these influencers’ background and history to find hints of past or current criminal activity.
Identifying ghost users using social media metadata - University College LondonGreg Kawere
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information a joint research project of the Alan Turing Institute and University College in London
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionIJERA Editor
This paper involves two approaches for finding the trending topics in social networks that is key-based approach and link-based approach. In conventional key-based approach for topics detection have mainly focus on frequencies of (textual) words. We propose a link-based approach which focuses on posts reflected in the mentioning behavior of hundreds users. The anomaly detection in the twitter data set is carried out by retrieving the trend topics from the twitter in a sequential manner by using some API and corresponding user for training, then computed anomaly score is aggregated from different users. Further the aggregated anomaly score will be feed into change-point analysis or burst detection at the pinpoint, in order to detect the emerging topics. We have used the real time twitter account, so results are vary according to the tweet trends made. The experiment shows that proposed link-based approach performs even better than the keyword-based approach.
Classification of instagram fake users using supervised machine learning algo...IJECEIAES
On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.
A DATA MINING APPROACH FOR FILTERING OUT SOCIAL SPAMMERS IN LARGE-SCALE TWITT...ijaia
Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise
platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter
draws many spammers or other fake accounts to post malicious links and infiltrate legitimate users'
accounts with many spam messages. Therefore, it is crucial to recognize and screen spam tweets and spam
accounts. As a result, spam detection is highly needed but still a difficult challenge. This article applied
several Bio-inspired optimization algorithms to reduce the features' dimensions in the first stage. Then we
used several classification schemes in the second stage to enhance the spam detection rate in three real
Twitter data collections. The performance of the chosen classifiers also revealed that Random Forest and
C4.5 classifiers achieved the highest Accuracy, Precision, Recall, and F1-score even on class imbalance.
Identifying ghost users using social media metadata - University College LondonGreg Kawere
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information a joint research project of the Alan Turing Institute and University College in London
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionIJERA Editor
This paper involves two approaches for finding the trending topics in social networks that is key-based approach and link-based approach. In conventional key-based approach for topics detection have mainly focus on frequencies of (textual) words. We propose a link-based approach which focuses on posts reflected in the mentioning behavior of hundreds users. The anomaly detection in the twitter data set is carried out by retrieving the trend topics from the twitter in a sequential manner by using some API and corresponding user for training, then computed anomaly score is aggregated from different users. Further the aggregated anomaly score will be feed into change-point analysis or burst detection at the pinpoint, in order to detect the emerging topics. We have used the real time twitter account, so results are vary according to the tweet trends made. The experiment shows that proposed link-based approach performs even better than the keyword-based approach.
Classification of instagram fake users using supervised machine learning algo...IJECEIAES
On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17 features used, based on these sources: 6 metadata, 3 media info, 2 engagement, 2 media tags, 4 media similarity Five machine learning algorithms will be tested. Three different approaches of classification are proposed, i.e. classification to 2-classes and 4-classes, and classification with metadata. Random forest algorithm produces the highest accuracy for the 2-classes (authentic, fake) and 4-classes (authentic, active fake user, inactive fake user, spammer) classification, with accuracy up to 91.76%. The result also shows that the five metadata variables, i.e. number of posts, followers, biography length, following, and link availability are the biggest predictors for the users class. Additionally, descriptive statistics results reveal noticeable differences between fake and authentic users.
A DATA MINING APPROACH FOR FILTERING OUT SOCIAL SPAMMERS IN LARGE-SCALE TWITT...ijaia
Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise
platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter
draws many spammers or other fake accounts to post malicious links and infiltrate legitimate users'
accounts with many spam messages. Therefore, it is crucial to recognize and screen spam tweets and spam
accounts. As a result, spam detection is highly needed but still a difficult challenge. This article applied
several Bio-inspired optimization algorithms to reduce the features' dimensions in the first stage. Then we
used several classification schemes in the second stage to enhance the spam detection rate in three real
Twitter data collections. The performance of the chosen classifiers also revealed that Random Forest and
C4.5 classifiers achieved the highest Accuracy, Precision, Recall, and F1-score even on class imbalance.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.