The document describes the various metrics and exports available from the Twitter Capture and Analysis Toolkit (TCAT). TCAT can generate metrics on tweet statistics, user activity, hashtag usage, mention networks, and more. It exports data in CSV and network graph formats for analyzing characteristics of Twitter data collected by keyword searches.
Twitter is a free social networking microblogging service that allows registered members to broadcast, in real-time, short posts called tweets. Twitter members can broadcast tweets and follow other users’ tweets by using multiple devices, making this information system one of the fastest in the world. In this chapter, we leverage this characteristic to introduce a novel topic-detection method aimed at informing, in real-time, a specific user about the most emerging arguments expressed by the network around his/her domain interests. With this goal, we aim at formalizing the information spread over the network by studying the topology of the network and by modeling the implicit and explicit connections among the users. Then, we propose an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user. We finally test the proposed model through various experiments and user studies.
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
Social media is attracting global crowd rapidly. In websites such as Facebook, twitter etc one can share, view, like posts, such as images, videos, texts. Users also interact with each other. Communities are part of few such social networking websites. In a community people can learn more about their area of interest, share information on those topics, discuss about their perspectives etc. This paper recommends how community can be suggested to a user based on enhanced quasi clique technique.
Prediction of Reaction towards Textual Posts in Social NetworksMohamed El-Geish
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Event detection and summarization based on social networks and semantic query...ijnlc
Events can be characterized by a set of descriptive, collocated keywords extracted documents. Intuitively,
documents describing the same event will contain similar sets of keywords, and the graph for a document collection will contain clusters individual events. Helping users to understand the event is an acute problem nowadays as the users are struggling to keep up with tremendous amount of information published every day in the Internet. The challenging task is to detect the events from online web resources, it is getting more attentions. The important data source for event detection is a Web search log because the information it contains reflects users’ activities and interestingness to various real world events. There are three major issues playing role for event detection from web search logs: effectiveness, efficiency of
detected events. We focus on modeling the content of events by their semantic relations with other events
and generating structured summarization. Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization provides a comprehensible explanation of the event sequence based on certain aspects.
Twitter is a free social networking microblogging service that allows registered members to broadcast, in real-time, short posts called tweets. Twitter members can broadcast tweets and follow other users’ tweets by using multiple devices, making this information system one of the fastest in the world. In this chapter, we leverage this characteristic to introduce a novel topic-detection method aimed at informing, in real-time, a specific user about the most emerging arguments expressed by the network around his/her domain interests. With this goal, we aim at formalizing the information spread over the network by studying the topology of the network and by modeling the implicit and explicit connections among the users. Then, we propose an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user. We finally test the proposed model through various experiments and user studies.
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
Social media is attracting global crowd rapidly. In websites such as Facebook, twitter etc one can share, view, like posts, such as images, videos, texts. Users also interact with each other. Communities are part of few such social networking websites. In a community people can learn more about their area of interest, share information on those topics, discuss about their perspectives etc. This paper recommends how community can be suggested to a user based on enhanced quasi clique technique.
Prediction of Reaction towards Textual Posts in Social NetworksMohamed El-Geish
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Event detection and summarization based on social networks and semantic query...ijnlc
Events can be characterized by a set of descriptive, collocated keywords extracted documents. Intuitively,
documents describing the same event will contain similar sets of keywords, and the graph for a document collection will contain clusters individual events. Helping users to understand the event is an acute problem nowadays as the users are struggling to keep up with tremendous amount of information published every day in the Internet. The challenging task is to detect the events from online web resources, it is getting more attentions. The important data source for event detection is a Web search log because the information it contains reflects users’ activities and interestingness to various real world events. There are three major issues playing role for event detection from web search logs: effectiveness, efficiency of
detected events. We focus on modeling the content of events by their semantic relations with other events
and generating structured summarization. Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization provides a comprehensible explanation of the event sequence based on certain aspects.
A Machine Learning Approach for Send Time Optimization Ahmad Ali Abin
An efficient framework for Send Time Optimization was proposed in this presentation by profiling and segmentation of recipients. Also, machine learning approaches were used to predict and analyze the recipients' behaviors based on the available contents by considering any prior knowledge about the recipients.
What Sets Verified Users apart? Insights Into, Analysis of and Prediction of ...IIIT Hyderabad
Social network and publishing platforms, such as Twitter, support the concept of verification. Veri-
fied accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tan-
tamount to endorsement. However, a significant body of prior work suggests that possessing a verified
status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a station
is highly coveted among public figures and influencers. Hence, we attempt to characterize the network
of verified users on Twitter and compare the results to similar analyses performed for the entire Twit-
ter network. We extracted the whole graph of verified users on Twitter (as of July 2018) and obtained
231,246 English user-profiles and 79,213,811 connections. Subsequently, in the network analysis, we
found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects, such as
possessing a short diameter. However, our findings contrast with earlier results on multiple fronts, such
as the possession of a power-law out-degree distribution, slight dissortativity, and a significantly higher
reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient com-
ponents within this sub-graph and detect the absence of homophily with respect to popularity, which
again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series
of verified user activity levels.
It is in this backdrop that we attempt to deconstruct the extent to which Twitter’s verification policy
mingles the notions of authenticity and authority. To this end, we seek to unravel the aspects of a user’s
profile, which likely engender or preclude verification. The aim of the paper is two-fold: First, we test
if discerning the verification status of a handle from profile metadata and content features is feasible.
Second, we unravel the characteristics which have the most significant bearing on a handle’s verification
status. We augmented our dataset with all the 494 million tweets of the aforementioned users over a one
year collection period along with their temporal social reach and activity characteristics. Our proposed
models are able to reliably identify verification status (Area under curve AUC > 99%). We show that
the number of public list memberships, presence of neutral sentiment in tweets and an authoritative
language style are the most pertinent predictors of verification status.
To the best of our knowledge, this work represents the first quantitative attempt at characterizing
verified users on Twitter and also the first attempt at discerning and classifying verification worthy users
on Twitter.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when
you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data
sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms
semantic technologies and social computing environment. Recommender system is a subclass of decision support system.
Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this
paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified
recommendations along with semantic web and also discussed about major thrust areas of research in each category.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms semantic technologies and social computing environment. Recommender system is a subclass of decision support system. Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified recommendations along with semantic web and also discussed about major thrust areas of research in each category.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when
you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data
sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms
semantic technologies and social computing environment. Recommender system is a subclass of decision support system.
Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this
paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified
recommendations along with semantic web and also discussed about major thrust areas of research in each category.
Popsters. Social media content analytics toolArseniy Kushnir
Popsters is a social media content analytics tool. It helps fast and simple to know what kind of posts are better, compare different social pages and to make report of social media activity. It`s fast, convinient and simple to use. Just 2 min to analyse any page.
Tweet Summarization and Segmentation: A Surveyvivatechijri
The use of social media is increasing day by day. It has become an important medium for getting
information about current happenings around the world. Among various social media platforms, with millions
of users, twitter is one of the most prominent social networking site. Over the years sentiment analysis is being
performed on twitter to understand what tweets that are posted mean. The purpose of this paper is to survey
various tweet segmentation and summarization techniques and the importance of Particle Swarm Optimization
(PSO) algorithm for tweet summarization [1][2].
A Machine Learning Approach for Send Time Optimization Ahmad Ali Abin
An efficient framework for Send Time Optimization was proposed in this presentation by profiling and segmentation of recipients. Also, machine learning approaches were used to predict and analyze the recipients' behaviors based on the available contents by considering any prior knowledge about the recipients.
What Sets Verified Users apart? Insights Into, Analysis of and Prediction of ...IIIT Hyderabad
Social network and publishing platforms, such as Twitter, support the concept of verification. Veri-
fied accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tan-
tamount to endorsement. However, a significant body of prior work suggests that possessing a verified
status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a station
is highly coveted among public figures and influencers. Hence, we attempt to characterize the network
of verified users on Twitter and compare the results to similar analyses performed for the entire Twit-
ter network. We extracted the whole graph of verified users on Twitter (as of July 2018) and obtained
231,246 English user-profiles and 79,213,811 connections. Subsequently, in the network analysis, we
found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects, such as
possessing a short diameter. However, our findings contrast with earlier results on multiple fronts, such
as the possession of a power-law out-degree distribution, slight dissortativity, and a significantly higher
reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient com-
ponents within this sub-graph and detect the absence of homophily with respect to popularity, which
again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series
of verified user activity levels.
It is in this backdrop that we attempt to deconstruct the extent to which Twitter’s verification policy
mingles the notions of authenticity and authority. To this end, we seek to unravel the aspects of a user’s
profile, which likely engender or preclude verification. The aim of the paper is two-fold: First, we test
if discerning the verification status of a handle from profile metadata and content features is feasible.
Second, we unravel the characteristics which have the most significant bearing on a handle’s verification
status. We augmented our dataset with all the 494 million tweets of the aforementioned users over a one
year collection period along with their temporal social reach and activity characteristics. Our proposed
models are able to reliably identify verification status (Area under curve AUC > 99%). We show that
the number of public list memberships, presence of neutral sentiment in tweets and an authoritative
language style are the most pertinent predictors of verification status.
To the best of our knowledge, this work represents the first quantitative attempt at characterizing
verified users on Twitter and also the first attempt at discerning and classifying verification worthy users
on Twitter.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when
you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data
sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms
semantic technologies and social computing environment. Recommender system is a subclass of decision support system.
Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this
paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified
recommendations along with semantic web and also discussed about major thrust areas of research in each category.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms semantic technologies and social computing environment. Recommender system is a subclass of decision support system. Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified recommendations along with semantic web and also discussed about major thrust areas of research in each category.
A Survey on Decision Support Systems in Social MediaEditor IJCATR
Web 3.0 is the upcoming phase in web evolution. Web 3.0 will be about “feeding you the information that you want, when
you want it” i.e. personalization of the web. In web 3.0 the basic principle is linking, integrating and analyzing data from various data
sources into new information streams by means of semantic technology. So, we can say that Web 3.0 comprises of two platforms
semantic technologies and social computing environment. Recommender system is a subclass of decision support system.
Recommendations in social web are used to personalize the web [20]. Social Tagging System is one type of social media. In this
paper we present the survey of various recommendations in Social Tagging Systems (STSs) like tag, item, user and unified
recommendations along with semantic web and also discussed about major thrust areas of research in each category.
Popsters. Social media content analytics toolArseniy Kushnir
Popsters is a social media content analytics tool. It helps fast and simple to know what kind of posts are better, compare different social pages and to make report of social media activity. It`s fast, convinient and simple to use. Just 2 min to analyse any page.
Tweet Summarization and Segmentation: A Surveyvivatechijri
The use of social media is increasing day by day. It has become an important medium for getting
information about current happenings around the world. Among various social media platforms, with millions
of users, twitter is one of the most prominent social networking site. Over the years sentiment analysis is being
performed on twitter to understand what tweets that are posted mean. The purpose of this paper is to survey
various tweet segmentation and summarization techniques and the importance of Particle Swarm Optimization
(PSO) algorithm for tweet summarization [1][2].
Snatz is a data mining instrument. It can recognizes semantic of news content using NLP algorithm
On the basis of acquired summary SNATZ can define new knowledges:
-detect new Summary sets;
-gathering Trends statistics;
-opportunity to build Segments;
-using new Keywords as Terms for training NLP algorithm;
-making recommendation of news from different Segments;
Our solutions allow to change the paradigm of ‘Collaborative Filtering’.
Tweet are being created short text message and shared for both users and data analysts. Twitter which receive
over 400 million tweets per day has emerged as an invaluable source of news, blogs, opinions and more. our
proposed work consists three components tweet stream clustering to cluster tweet using k-means cluster
algorithm and second tweet cluster vector technique to generate rank summarization using greedy algorithm,
therefore requires functionality which significantly differ from traditional summarization . in general, tweet
summarization and third to detect and monitors the summary-based and volume based variation to produce
timeline automatically from tweet stream. Implementing continuous tweet stream reducing a text document is
however not an simple task, since a huge number of tweets are worthless, unrelated and raucous in nature, due
to the social nature of tweeting. Further, tweets are strongly correlated with their posted instance and up-to-theminute
tweets tend to arrive at a very fast rate. Efficiency—tweet streams are always very big in level, hence the
summarization algorithm should be greatly capable; Flexibility—it should provide tweet summaries of random
moment durations. (3) Topic evolution—it should routinely detect sub-topic changes and the moments that they
happen.
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)paperpublications3
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
Task 803 - 1 page Instructions Distinguish between full con.docxrudybinks
Task 803 - 1 page
Instructions: Distinguish between full content data (including collection tools), session data (including collection tools) and statistical data (including collection tools)
Use examples from the readings, or from your own research, to support your views, as appropriate. Encouraged to conduct research and use other sources to support your answers. Be sure to list your references at the end. References must be in APA citation format. A minimum of 250-300 words.
Number of Pages: 1 Page
Page Line Spacing: Double spaced (Default)
Academic Level: College
Paper Format: APA
Task 804
1.
Write 150 word replies to each of the following:
Add additional insight opinions or challenge opinions and you can visit a couple of the web sites contributed and share your opinion of these sites. Minimum of 150 words for each.
Part 1 (respond in 150 words)
1) Session data, which can be obtained through full content data, summarizes pack exchange. The data is take from a flow, or a session, and allows analysis of source IP, source port, destination IP, destination port, the timestamp, and the overall information measurement exchanged during the session. The session first method is predicated on collecting all of the data, then summarizing all of the data as a conversation. This method is expected to work best on busy networks, where the method allows for quicker parsing of the data by an analyst, and allows for specific movement tracking.
Statistical data, on the other hand, is a way to look at a network that takes into account the normal behaviors and observed parameters of that network using descriptive statistics. This data identifies the patterns of overall traffic flow and gives the analyst the ability spot anomalies. Beyond that, these statistics can be used to identify potential inefficiencies and reallocate resources.
Each of these types of data have different tools available to collect and compile.
For full content data, the tools recommended are LIBPCAP, TCPDUMP, Tethereal, Snort, and Ethereal. Of these, LIBPCAP seems to be the foundation, as well as TCPDUMP, as the other tools seem to take those two programs and integrate them into their setups. Each provides their own format for packet data, and some allow you to go deeper into the data to pull out hexadecimal and ASCII data, including Tethereal, Ethereal, and Snort. Ethereal also has the ability to reconstruct streams.
For session data, tools use probes, collectors and consoles, working in concert to find, collate, and translate the data provided. The text recommends Cisco Net Flow due to the wide-spread use of Cisco technology, and the program’s compatibility with the many open source tools Mr. Bejtlich represents. This data can then be viewed through TCPDUMP. Some other open source collection tools include FProbe, NG_Netflow, Softflowd Pfflowd, and Ntop. Mr. Bejtlich also mentions Flow Tools, Flow Capture, Flow-Cat and Flow-Print (complimentary tools), Sf ...
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. SMAC LAB, LSU
Sep 28, 2018
SMAC Talks
TCAT
Instructor: Dr. Ke (Jenny) Jiang
2. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 1: User Stats (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
# of tweets, tweets with links, tweets with hashtags, tweets with
mentions, retweets, replies
Get a feel for the overall characteristics of your data set
3. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 2: User Stats Overall(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains the min, max, average, Q1, median, Q3, and trimmed mean for:
number of tweets per user, urls per user, number of followers, number of
friends, number of tweets
5. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 3: User Stats Individual(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Lists users and their number of tweets, number of followers, number of
friends, how many times they are listed, their UTC time offset, whether
the user has a verified account and how many times they appear in the
data set.
8. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 4: Hashtag frequency(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
find out which hashtags are most often associated with your subject.
9. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 5: Hashtag-user activity(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Lists hashtags, the number of tweets with that hashtag, the number of
distinct users tweeting with that hashtag, the number of distinct
mentions tweeted together with the hashtag, and the total number of
mentions tweeted together with the hashtag.
10. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 6: Twitter client (source) frequency(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
List the frequency of tweet software sources per interval.
11. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 7:
Twitter client (source) stats (individual)(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Lists sources and their number of tweets, retweets, hashtags, URLs and mentions
12. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 8:
User visibility (mention frequency)(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Lists usernames and the number of times they were mentioned by others.
find out which users are "influentials"
13. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 9:
User activity (tweet frequency)(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Lists usernames and the amount of tweets posted.
find the most active tweeters
see if the dataset is dominated by certain twitterati.
14. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 10:
User activity + visibility (tweet+mention frequency)(.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Lists usernames and the amount of tweets posted.
see wether the users mentioned are also those who tweet a lot
15. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 11:
Url frequency (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains the frequencies of tweeted URLs.
find out which contents (articles, videos, etc.) are referenced most often
16. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 12:
Host name frequency (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains the frequencies of tweeted domain names.
find out which sources (media, platforms, etc.) are referenced most often
17. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 13:
Identical tweet frequency (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains tweets and the number of times they have been (re)tweeted identically
get a grasp of the most "popular" content
18. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 14:
Word frequency (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains words and the number of times they have been used
get a grasp of the most used language
19. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 15:
Media frequency (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains media URLs and the number of times they have been used
get a grasp of the most popular media
20. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet Statistics and Activity Metrics 16:
Export table with potential gaps in your data (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Exports a spreadsheet with all known data gaps in your current query, during which
TCAT was not running or capturing data for this bin
Gain insight in possible missing data due to outages
21. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet exports 1:
Random set of tweets from selection (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains 1000 randomly selected tweets and information about them (user, date
created, from_user_name, retweet_count, favorite_count, lang, to_user_name
in_reply_to_status_id, quoted_status_id source, location, lat, lng, from_user_id
from_user_realname, from_user_verified, from_user_description, from_user_url,
from_user_profile_image_url, from_user_timezone, from_user_tweetcount
from_user_followercount, from_user_friendcount, from_user_favourites_count
from_user_listed, from_user_created_at)
a random subset of tweets is a representative sample that can be manually
classified and coded much more easily than the full set
22. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet exports 2:
List each individual retweet (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains all tweets and information about them (user, date created, ...)
spend time with your data
23. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet exports 3:
List each individual retweet (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Lists all retweets (and all the tweets metadata like follower_count)
chronologically.:RT @
This script is slow. Small datasets only!
24. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet exports 4:
Only tweets with lat/lon (.csv)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains only geo-located tweets
Geo location is different from the self-reported location
26. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet exports 7:
Export mentions table (tweet id, user from id, user from name, user to
id, user to name, mention, mention type)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains tweet ids from your selection, with mentions and the mention type.
Mention network
27. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Tweet exports 8:
Export URLs table (tweet id, url, expanded url, followed url)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Contains tweet ids from your selection and URLs.
28. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 1: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Social graph by mentions
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces a directed graph based on interactions between users. If a users
mentions another one, a directed link is created. The more often a user
mentions another, the stronger the link ("link weight"). The "count" value
contains the number of tweets for each user in the specified period.
analyze patterns in communication, find "hubs" and "communities",
categorize user accounts.
29. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 2: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Social graph by in_reply_to_status_id
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces a directed graph based on interactions between users. If a tweet
was written in reply to another one, a directed link is created.
analyze patterns in communication, find "hubs" and "communities",
categorize user accounts.
30. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 3: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Co-hashtag graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces an undirected graph based on co-word analysis of hashtags. If
two hashtags appear in the same tweet, they are linked. The more often they
appear together, the stronger the link ("link weight").
explore the relations between hashtags, find and analyze sub-issues,
distinguish between different types of hashtags (event related, etc.).
31. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 4: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Bipartite hashtag-mention graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces a bipartite graph based on co-occurence of hashtags and
@mentions. If an @mention co-occurs in a tweet with a certain hashtag,
there will be a link between that @mention and the hashtag. The more often
they appear together, the stronger the link ("link weight").
explore the relational activity between mentioned users and hashtags,
find and analyze which users are considered experts around which
topics.
32. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 5: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Bipartite hashtag-source graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces a bipartite graph based on co-occurence of hashtags and
"sources" (the client a tweet was sent from is its source) . If a hashtag is
tweeted from a particular client, there will be a link between that client and
the hashtag. The more often they appear together, the stronger the link ("link
weight").
explore the relations between clients and hashtags, find and analyze
which clients are related to which topics.
33. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 6: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
user-source graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces a bipartite graph based on co-occurence of users and
"sources" (the client a tweet was sent from is its source) . If a users tweets
from a particular client, there will be a link between that client and the user.
The more often they appear together, the stronger the link ("link weight").
explore the relations between clients and users, find and analyze which
users use which clients.
34. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 7: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Bipartite domain-source graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces a bipartite graph based on co-occurence of (URL-)domains and
"sources" (the client a tweet was sent from is its source) . If a domain is
tweeted from a particular client, there will be a link between that client and
the domain. The more often they appear together, the stronger the link ("link
weight").
explore the relations between domains and hashtags, find and analyze
which domains are related to which sources.
35. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 8: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Bipartite URL-user graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces a bipartite graph based on co-occurence of URLS and users. If a
user wrote a tweet with a certain URL, there will be a link between that user
and the URL. The more often they appear together, the stronger the link
("link weight").
explore the relations between users and URLs, find and analyze which
users group around which URLs.
36. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 8: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Bipartite hashtag-URL graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Creates a .csv file that contains URLs and the number of times they have
co-occured with a particular hashtag.
Creates a .gexf file that contains a bipartite graph (.gexf, open in gephi)
based on co-occurence of URLs and hashtags. If a URL co-occurs with a
certain hashtag, there will be a link between that URL and the hashtag. The
more often they appear together, the stronger the link ("link weight").
get a grasp of how urls are qualified
37. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Networks 9: All network exports come as .gexf or .gdf files which you can
open in Gephi or similar
Bipartite hashtag-host (domain) graph
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Creates a .csv file that contains hosts and the number of times they have
co-occured with a particular hashtag.
Creates a .gexf file that contains a bipartite graph (.gexf, open in gephi)
based on co-occurence of hosts and hashtags. If a hosts co-occurs with a
certain hashtag, there will be a link between that host and the hashtag. The
more often they appear together, the stronger the link ("link weight").
get a grasp of how hosts are qualified
38. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Experimental 1:
Cascade
(overall, /min, /hour, /day, /week, /month, /year, custom…)
User accounts are distributed vertically; tweets - shown as dots - are spread
out horizontally over time. Lines indicate retweets..
visually explore temporal structures and retweets patterns.
39. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Experimental 1:
Cascade
(overall, /min, /hour, /day, /week, /month, /year, custom…)
User accounts are distributed vertically; tweets - shown as dots - are spread
out horizontally over time. Lines indicate retweets.
visually explore temporal structures and retweets patterns.
40. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Experimental 2:
The Sankey Maker
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces an alluvial diagram. Alluvial diagrams are a type of flow diagram
originally developed to represent changes in network structure over time.
plot the relation between various fields such as from_user_lang,
hashtags or Twitter client
41. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Experimental 2:
The Sankey Maker
(overall, /min, /hour, /day, /week, /month, /year, custom…)
42. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Experimental 3:
Associational profile (hashtags)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces an associational profile as well as a time-encoded co-hashtag
network.
explore shifts in hashtags associations
43. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Experimental 3:
Associational profile (hashtags)
(overall, /min, /hour, /day, /week, /month, /year, custom…)
Produces an associational profile as well as a time-encoded co-hashtag
network.
explore shifts in hashtags associations
44. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Experimental 3:
Associational profile (hashtags)
explore shifts in hashtags associations
45. TCAT
Twitter Capture and Analysis Toolkit (DMI-TCAT) - By Keywords
Please
visit
this
TCAT
installa/on
at
these
URLs:
h5p://18.223.107.254/analysis/
TCAT
standard
login
(for
analysis
only):
Username:
tcat
Password:
FTHnX73cFuUVp7KyVzGZLxdkLPSEp7KCMc