We all have mental representations of events: physical or cognitive processes - yet how we create, retain and negotiate them as frames is a matter of how well we do perform causal reasoning of the dynamics involved. But how we could keep track of them when it comes to interaction? how people do argue about causal explanations where a multitude of actors and variables are interplaying? If just quantifying these features is a compelling yet jeopardizing investigation - keeping track of how they do evolve in time and we could retrieve and forecast information diffusion and contagion is where complex networks analysis meets cognitive semiotics, behavioral studies, pragmatic theories, and NLP methods.
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
Tweet Segmentation and Its Application to Named Entity Recognition1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Our project is about guessing the correct missing
word in a given sentence. To find of guess the missing word
we have two main methods one of them statistical language
modeling, while the other is neural language models.
Statistical language modeling depend on the frequency of the
relation between words and here we use Markov chain. Since
neural language models uses artificial neural networks which
uses deep learning, here we use BERT which is the state of art
in language modeling provided by google.
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
Tweet Segmentation and Its Application to Named Entity Recognition1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Our project is about guessing the correct missing
word in a given sentence. To find of guess the missing word
we have two main methods one of them statistical language
modeling, while the other is neural language models.
Statistical language modeling depend on the frequency of the
relation between words and here we use Markov chain. Since
neural language models uses artificial neural networks which
uses deep learning, here we use BERT which is the state of art
in language modeling provided by google.
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...kevig
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...ijnlc
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while
scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
A COMPARATIVE STUDY OF SOCIAL NETWORKING APPROACHES IN IDENTIFYING THE COVERT...ijwscjournal
This paper categories and compares various works done in the field of social networking for covert networks. It uses criminal network analysis to categorize various approaches in social engineering like dynamic network analysis, destabilizing covert networks, counter terrorism, key player, subgroup detection
and homeland security. The terrorist network has been taken for study because of its network of individuals who spread from continents to continents and have an effective influence of their ideology throughout the globe. It also presents various metrics based on which the centrality of nodes in the graphs could be
identified and it’s illustrated based on a synthetic dataset for 9/11 attack. This paper will also discuss various open problems in this area.
Online Social Networks have become a prominent mode of communication and collaboration. Link Prediction is a major issue in Social Networks. Though ample methods are proposed to solve it, most of them take a static view of the network. Social Networks are dynamic in nature, this aspect has to be accounted. In this paper we propose a novel predictor LCF for Link Prediction in dynamic networks. In this method we view Social Networks as sequence of snapshots, each snapshot is the state of the network of a particular time period. Each edge of the network is assigned a weight based on its time stamp. We compute the LCF score for all node pairs in the network to predict the associations that may occur at a future time in the Social Network. We have also shown that our predictor outperforms the standard baseline methods for Link Prediction
Exploring the Current Trends and Future Prospects in Terrorist Network Mining cscpconf
In today’s era of hi-tech technologies, criminals are easily fulfilling their inhuman goals against
the mankind. Thus, the security of civilians has significantly become important. In this regard,
the law-enforcement agencies are aiming to prevent future attacks. To do so, the terrorist
networks are being analyzed using data mining techniques. One such technique is Social network analysis which studies terrorist networks for the identification of relationships and associations that may exist between terrorist nodes. Terrorist activities can also be detected by means of analyzing Web traffic content. This paper studies social network analysis, web traffic content and explores various ways for identifying terrorist activities.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Sending out an SOS (Summary of Summaries): A Brief Survey of Recent Work on A...Griffin Adams
Research on the evaluation of abstractive summarization models has evolved considerably in the last few years. To take stock of these changes, namely, the shift from n-gram overlap to fact-based assessments, I have written a brief summary of papers on evaluation metrics, most of which focuses on the period from 2018 to mid-2020. The paper is written in a terse literature review format, so as to aid researchers when craft- ing related works sections of papers on summary evaluation.
Topic Modeling for Information Retrieval and Word Sense Disambiguation tasksLeonardo Di Donato
Experimental work done regarding the use of Topic Modeling for the implementation and the improvement of some common tasks of Information Retrieval and Word Sense Disambiguation.
First of all it describes the scenario, the pre-processing pipeline realized and the framework used. After we we face a discussion related to the investigation of some different hyperparameters configurations for the LDA algorithm.
This work continues dealing with the retrieval of relevant documents mainly through two different approaches: inferring the topics distribution of the held out document (or query) and comparing it to retrieve similar collection’s documents or through an approach driven by probabilistic querying. The last part of this work is devoted to the investigation of the word sense disambiguation task.
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...kevig
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...ijnlc
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while
scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
A COMPARATIVE STUDY OF SOCIAL NETWORKING APPROACHES IN IDENTIFYING THE COVERT...ijwscjournal
This paper categories and compares various works done in the field of social networking for covert networks. It uses criminal network analysis to categorize various approaches in social engineering like dynamic network analysis, destabilizing covert networks, counter terrorism, key player, subgroup detection
and homeland security. The terrorist network has been taken for study because of its network of individuals who spread from continents to continents and have an effective influence of their ideology throughout the globe. It also presents various metrics based on which the centrality of nodes in the graphs could be
identified and it’s illustrated based on a synthetic dataset for 9/11 attack. This paper will also discuss various open problems in this area.
Online Social Networks have become a prominent mode of communication and collaboration. Link Prediction is a major issue in Social Networks. Though ample methods are proposed to solve it, most of them take a static view of the network. Social Networks are dynamic in nature, this aspect has to be accounted. In this paper we propose a novel predictor LCF for Link Prediction in dynamic networks. In this method we view Social Networks as sequence of snapshots, each snapshot is the state of the network of a particular time period. Each edge of the network is assigned a weight based on its time stamp. We compute the LCF score for all node pairs in the network to predict the associations that may occur at a future time in the Social Network. We have also shown that our predictor outperforms the standard baseline methods for Link Prediction
Exploring the Current Trends and Future Prospects in Terrorist Network Mining cscpconf
In today’s era of hi-tech technologies, criminals are easily fulfilling their inhuman goals against
the mankind. Thus, the security of civilians has significantly become important. In this regard,
the law-enforcement agencies are aiming to prevent future attacks. To do so, the terrorist
networks are being analyzed using data mining techniques. One such technique is Social network analysis which studies terrorist networks for the identification of relationships and associations that may exist between terrorist nodes. Terrorist activities can also be detected by means of analyzing Web traffic content. This paper studies social network analysis, web traffic content and explores various ways for identifying terrorist activities.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
Sending out an SOS (Summary of Summaries): A Brief Survey of Recent Work on A...Griffin Adams
Research on the evaluation of abstractive summarization models has evolved considerably in the last few years. To take stock of these changes, namely, the shift from n-gram overlap to fact-based assessments, I have written a brief summary of papers on evaluation metrics, most of which focuses on the period from 2018 to mid-2020. The paper is written in a terse literature review format, so as to aid researchers when craft- ing related works sections of papers on summary evaluation.
Topic Modeling for Information Retrieval and Word Sense Disambiguation tasksLeonardo Di Donato
Experimental work done regarding the use of Topic Modeling for the implementation and the improvement of some common tasks of Information Retrieval and Word Sense Disambiguation.
First of all it describes the scenario, the pre-processing pipeline realized and the framework used. After we we face a discussion related to the investigation of some different hyperparameters configurations for the LDA algorithm.
This work continues dealing with the retrieval of relevant documents mainly through two different approaches: inferring the topics distribution of the held out document (or query) and comparing it to retrieve similar collection’s documents or through an approach driven by probabilistic querying. The last part of this work is devoted to the investigation of the word sense disambiguation task.
Effective Semantics for Engineering NLP SystemsAndre Freitas
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
Presents Natural Language Processing (NLP) algorithms for for Bay Area NLP reading group. Survey of Probabilistic Topic Modeling such as Latent Dirichlet Allocation (LDA). Includes practical references explaining the algorithm along with software libraries for Python, Spark, and R.
Learning with me Mate: Analytics of Social Networks in Higher EducationDragan Gasevic
Effects of social interactions are reported in research on higher education to lead to positive outcomes such as higher levels of internalization, sense of community, academic achievement, metacognition, and student retention. The role of social networks has especially been emphasized in research due to the availability of theoretical foundations and analytic methods to investigate their effects in higher education. The increased use of technologies in education allows for the collection of large and rich datasets about social networks which call for the use of novel analytics methods. This talk will first give a brief overview of the existing work on and lessons learned from some well-known studies on social networks in higher education in diverse situations from face-to-face to massive open online courses. The talk will then identify critical challenges that require immediate attention in order for the study of social networks to make a sustainable impact on learning and teaching. The most important take away from the talk will be that
- computational aspects of the study of social networks need to be integrated deeply with theory, research and practice,
- novel methods for the study of critical dimensions (discourse, structure and dynamics) that shape network formation and network effects are necessary, and
- innovative instructional approaches are essential to address the changing conditions created by contemporary educational and technological contexts.
How to interpret NVivo/Cluster analysis/ results HennaAnsari
Interpretation of Cluster analysis
Content analysis
NVivo graphical analysis
qualitative analysis
Content analysis of leadership outlook and culture: Evidence from Public speaking skills and intentions
Cmaps as intellectual prosthesis (GERAS 34, Paris)Lawrie Hunter
At the present time, 'increasing accessibility of technology' is readily read as 'increasing accessibility of electronic information technology', but this is to ignore a history of pre-electronic technologies which have generally been conflated with the original media of education, first speech and rather later the writing of continuous text.
The insertion of spaces between words in text was a technology for accessibility of encoding. The paragraph was a technology for the signaling of rhetorical shifts. The bullet list is used for the representation of clusters of notions, either atomic (listing) or aggregates (classification). More substantial technological innovations include the data table and the graph.
One revolutionary technology that has not become mainstream in instructional communication is the Novakian concept map (i.e. the map whose links have text labels to specify the relation between two nodes). This technology has been substantially migrated to electronic information technology, and is arguably more prevalent there than in the traditional sphere, though it is still largely regarded as a novelty or non-essential element of instructional discourse.
This paper reports a case study of a fruitful application of Novakian mapping, wherein EAP learners of academic writing for management discover intellectual leverage in mapping, and develop their own use of the technique, in an iterative manner, in counterpoint with text analysis work. It tracks the cycling between moves analysis and concept mapping as these members of a graduate seminar work to unpack a paper that they have identified as a 'good model', but which they have realized is not a well-written paper.
The observations made here suggest that concept mapping is a pre-electronic technology that deserves a place amongst the essential tools for instructional discourse, particularly in settings such as EAP where the identification of rhetorical orchestration is difficult and where argument is often masked by other rhetorical devices.
AN EMPIRICAL STUDY OF WORD SENSE DISAMBIGUATIONijnlc
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performance of applications of computational linguistics such as machine translation, information
retrieval, text summarization, question answering systems, etc. We have presented a brief history of WSD,
discussed the Supervised, Unsupervised, and Knowledge-based approaches for WSD. Though many WSD
algorithms exist, we have considered optimal and portable WSD algorithms as most appropriate since they
can be embedded easily in applications of computational linguistics. This paper will also provide an idea of
some of the WSD algorithms and their performances, which compares and assess the need of the word
sense disambiguation.
We hosted a fantastic tutorial on Knowledge-infused Deep Learning at the 31st ACM Hypertext Conference on July 14. Broadly, the tutorial covered many exciting applications of Broad- and Community-based Knowledge Graph in Education, Clinical and Social-Media Healthcare, Pandemic, and Cryptomarkets.
We theorized the concept of Knowledge-infusion and showed its importance in gaining explainability and spectacular performance gains. We extended the idea of "Knowledge-infused Deep Learning" to Autonomous Driving, Cyber Social Harms, and DarkWeb.
The tutorial presentation with relevant resources and references are made online at http://kidl2020.aiisc.ai.
Organizational Identification of Millennial employees working remotely: Quali...HennaAnsari
The problem of practice for this study is to understand how Millennial employees identify with their organizations when working in a remote role. Understanding the employee experience could help us consider OID which is linked to range of positive employee outcomes, such as low turnover intention and higher engagement, as well as improved employee satisfaction, well-being, and employee performance (Ashforth, 2008 ). Actively disengaged employees manifest discontent by undermining more engaged employees’ efforts, and these workers can actively seek to harm the organization (Carrillo, 2017; Kompaso, 2010; Walden, 2017).
Predicting Forced Population Displacement Using News ArticlesJaresJournal
The world has witnessed mass forced population displacement across the globe. Population displacement has various indications, with different social and policy consequences. Mitigation of the humanitarian crisis requires tracking and predicting the population movements to
allocate the necessary resources and inform the policymakers. The set of events that triggers population movements can be traced in the news articles. In this paper, we propose the Population
Displacement-Signal Extraction Framework (PD-SEF) to explore a large news corpus and extract
the signals of forced population displacement. PD-SEF measures and evaluates violence signals,
which is a critical factor of forced displacement from it. Following signal extraction, we propose a
displacement prediction model based on extracted violence scores. Experimental results indicate
the effectiveness of our framework in extracting high quality violence scores and building accurate
prediction models.
Sentimental analysis is a context based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine learning techniques such as LSTM Long short term memory . This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM. Dirash A R | Dr. S K Manju Bargavi "LSTM Based Sentiment Analysis" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42345.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42345/lstm-based-sentiment-analysis/dirash-a-r
Similar to Modeling Causal Reasoning in Complex Networks through NLP: an Introduction (20)
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Modeling Causal Reasoning in Complex Networks through NLP: an Introduction
1. Modeling Causal Reasoning in Complex
Networks through NLP: an Introduction
How successful causal communication works?
How context and agents influence the relevance of causal features?
What about causal disagreements:
Linguistic ambiguity? Cognitive failure? Networks’ constraints?
And foremost, how to quantify all the variables interplaying?
Luca Nannini 11th October 2019
Cog Sem - Ling AU Symposium
1
3. Main questions:
How are causal representations refined and updated collectively in
communication?
How do causal disagreements arise, and how do conversational partners
interact to align their interpretations of causal events?
Modeling Causal Reasoning in Complex
Networks through NLP: an Introduction
3
5. 5 5
MA Thesis Research Project: Modeling mass entrainment as engagement and
semantic contagion in the 2016 U.S. first presidential debate live-tweeting
6. 6 6
MA Thesis Research Project: Mass attention as tweet volume, salient
moments of the debate
7. 7 7
MA Thesis Research Project: What is NLP?
AWFUL CODES,
BEAUTIFUL
STORIES
8. Natural Language Processing (NLP) in Data Science is about analyzing huge amounts of
text data for computationally elaborate insights on how human language is used - both on
lexical and semantic level, synchronically or diachronically.
NLP tasks rely on machine learning models that can be trained and tested with supervised
learning (e.g. classification and regression problems) or with unsupervised learning (e.g.
clustering and highlighting patterns)
8 8
MA Thesis Research Project: What is NLP?
9. Text information can be statistical: words count, words frequency, sentence length are
few of the lexical operations for quantifying lexicon and its usage.
This information can be syntactic too, such as chunking sentences and tagging
part-of-speech (POS tagging).
On a more advanced level, text information can be semantic: text classification in NLP is
about identifying the topics in a text - information retrieval, ranking documents, detecting if
an email it’s a spam or not, identifying if a review is positive or not (Sentiment Analysis),
correcting the spelling of a term or suggesting different verb tenses or nouns in a sentence
9 9
MA Thesis Research Project: What is NLP?
10. Topic Modeling is that subfield of NLP that deals with finding semantic
clusters (topics) and tendencies of words associations in text corpora.
The main models used are:
- Latent Semantic Analysis (LSA)
- Latent Dirichlet Allocation (LDA)
1010
MA Thesis Research Project: What is NLP? What is topic modeling?
11. Latent Dirichlet Allocation: a generative probabilistic model discovering
and classifying topics tendencies in text documents.
“Documents are represented as random mixtures over latent topics,
where each topic is characterized by a distribution over words”
(Blei, Ng, & Jordan, 2003).
“Don’t worry about it if you don’t understand”
Andrew Ng allows us to be dumb, no prob
MA Thesis Research Project: What is NLP? What is topic modeling? LDA?
1111
12. Each w in each d comes from a t and this t
is selected from a per-document distribution
over T. So we have two matrices:
1. ϴtd = P(t|d) which is the probability
distribution of topics in documents
2. Фwt = P(w|t) which is the probability
distribution of words in topics
Allocation: given
Dirichlet, allocate t to
d and w of d to t
Latent: don’t know a
priori - hidden in data.
MA Thesis Research Project: What is NLP? What is topic modeling? LDA?
1212
Dirichlet: distribution
of distributions. lol
Distribution of T in D
Distribution of W in T
13. - Text Corpora: collection of n documents
- Document: collection of n given topics distributed in a certain proportion
- Given a putative n Topics, the model segregates the keywords (w) distribution
along with the topics’ one
- Words are arranged according to un-/known parameters: e.g. the n topics given,
the variety of topics treated in the texts, the algorithm tuning parameters
1313
17. Distributional Hypothesis -
J. R. Firth, 1957: linguistics-based
hypothesis stating that words
co-occurring in the same lexical
contexts tend to be more
distributionally similar their
semantic meaning
Word Embeddings - Classifying words’ co-occurrences
MA Thesis Research Project: What is NLP? What is topic modeling? LDA? FastText?
1717
18. Word index sequences are read during
the training phase as embedding
vectors containing dense vectors of
multidimensional matrix values.
These dense vectors allocate the words’
location in the continuous vector space.
This continuous vector space is a
lower-dimensional space that preserves
semantic relationship encoding
embeddings’ position as distance and
vector direction.
Word Embeddings - Classifying words’ co-occurrences
MA Thesis Research Project: What is NLP? What is topic modeling? LDA? FastText?
Bag-of-Words (BOW)
Tokenization
(Normalization, stemming/lemmatization)
↓
Vectorization
(Assign numerical values through feature
selection)
=
Word Embeddings
Vector representation of tokens in a continuous
multidimensional vector space
1818
19. FastText: an extension of Word2Vec’s architecture released by Facebook AI Research in 2016 (Joulin, Grave,
Bojanowski, & Mikolov, 2016). FastText has also an open-source library working for text representations and text
classifiers with pre-trained word vector models available in several natural languages.
The main difference with Word2Vec is that FastText allows for representing the word occurrence chunking it in
several n-grams: the target word is replaced by a label. It returns rare words overcoming their morphological
inflection or other lexical derivations (prefix or suffix).
FastText aims to predict a category rather than predict a word due to an architecture of single layers based on
CBOW model for word representation. Further, this architecture is provided with a hierarchical softmax and not a
softmax over labels as Word2Vec - for a faster training phase
19 1
9
MA Thesis Research Project: What is NLP? What is topic modeling? LDA? FastText?
1919
20. 20 2
0
MA Thesis Research Project:
FastText word embedding of tweets
2020
22. 22 2
2
MA Thesis Research Project:
FastText word embedding of tweets
1. On the debate event (e.g. ‘tonight’, ‘presidential’,
‘debatenight’, ‘trump’, ’clinton’, ’show’).
2. ‘Social healing’ topic area with terms regarding racial
relations, police and marginal communities (‘race’, ‘police’,
‘plan’, ‘community’, ‘order’).
3.‘Achieving prosperity’ with words on tax policy, job
creation, economic deals and business investments (e.g.
‘job’, ‘tax’,’money’, ‘business’, ‘federal’, ‘pay’, ‘trillion’)
4. Clinton’s terms are reported, produced during the salient
moments of the two initial topic segments (‘hillary’, ‘hrc’,
‘email’, ‘fact-check’)
5. Live commentary of candidates image, with foremost bad
language (‘dumb’, ‘idiot’, ’crazy’, ’joke’, ’fuck’).
6. Live commentary of the debate per se (e.g. ‘interrupt’,
‘moderator’, ‘mention’, ‘speak’,’started’)plus references to
drinking games (e.g. “drink a shot every time someone
says…”).
7. Most common verbs used.
2222
23. 23 2
3
Surely - it sounds obvious that people tweet for fun, for
providing living commentary of candidate persona, for assess
their political leaning and discredit the opponents.
But live-tweeting is influenced by several variables
How do you account for...
- Social Cognition/Behavioral components of leadership
- Network Structures
- Time scale of engagement
- Group polarization (opinion leadership), selective exposure
- News diet, social cohesion
Surely - it sounds obvious that people tweet for fun, to provide
an informal living commentary of candidates’ persona, to assess
their political leaning and discredit the opponents.
But live-tweeting is influenced by several variables
2323
24. 24 2
4
There is a pilot group (A) that creates information and a target
group (B) that receive and tailor it according to several
endogenous and exogenous variables interplaying.
How to quantify them?
Linguistic, Cognitive, Network variables
Can we forecast how A rhetorical patterns will impact B?
B is composed of heterogeneous subgroups with different
exposure: How to detect them? What influences them?
Long story short - Limitations of my study
2424
26. 26 2
6
My current project:
OLaV
Data
Mining
↓
Preprocessing
↓
Wrangling
↓
Visualization
1.
Modeling Causal
Inference Computationally
● Social Media Mining
● Topic Modeling (NLP)
● LIWC Causal Analysis
● Train & Test Classifier
● Implementation for SCM
(Structural Causal Models)
2626
27. 27 2
7
RQ1: What linguistic, discursive, and interactional
patterns characterize pro- and anti-vaccine posts on
social media?
RQ2: How do anti-vaccine proponents construct
alternative causal explanations for recent
vaccine-related events like global measles outbreaks?
RQ3: How does the particular packaging of causal
information about vaccine-preventable outbreaks affect
subjects’ interpretation of the information?
OLaV “Online Language of Vaccines: A mixed-methods
cross-cultural study of the vaccination debate on social media”
AU LICS Department - Alexandra Regina Kratschmer, Rebekah
Brita Baglini, Byurakn Ishkhanyan, Ana Paulla Braga Mattos
Check us out on:
- Twitter, @OLaV_AU
- GitHub, olav-au.github.io/project/
2727
28. 28 2
8
Our current approach:
LIWC + NLP = detecting vaccine stances on tweets
Take 10% of the vaccine tweets datasets with highest LIWC
causation values →
Pipeline: train a classifier for detecting causal stances, i.e.
assessing polarity (pro- / anti-) through the association of lexical
causatives and other lexicon →
Integrate a Structural Causal Model for retrieving causal dynamics
Linguistic Inquiry and Word Count (LIWC)
2828
30. 30 3
0
BUT modeling causal reasoning is not descriptive:
It’s about retrieving and quantifying inferential processes →
i.e. modeling the causes that contributed to output the actual effect →
i.e. the linguistic, cognitive and network features that contributed in shaping a given
linguistic and/or rhetorical pattern
NLP methods are foremost descriptive:
1. Scraping text data online
2. Preprocessing them (a delicate task)
3. Analyzing with already attuned models (foremost)
4. Present them
OLaV “Online Language of Vaccines: A mixed-methods
cross-cultural study of the vaccination debate on social media”
AU LICS Department - Alexandra Regina Kratschmer, Rebekah
Brita Baglini, Byurakn Ishkhanyan, Ana Paulla Braga Mattos
3030
32. 32 3
2
BUT modeling causal reasoning is not descriptive:
It’s about retrieving and quantifying inferential processes →
i.e. modeling the causes that contributed to output the actual effect →
i.e. the linguistic, cognitive and network features that contributed in shaping a given
linguistic and/or rhetorical pattern
The Fundamental Problem of Causal
Inference, Rubin 1988
What are the treatments causal effect on a particular
individual as measured by an outcome?
Problem: we are not able to see the counterfactuals
from a single outcome - we have to advance inferences
3232
34. 34 3
4
Ladder of Causation,
Pearl 2018
I. Association can have no
causal implications
II. Intervention is assessing
causality by experimentally
performing some action
that affects one of the
observed events
III. Counterfactual level is
about inferring alternate
causal version of a past
event
3434
36. 36 3
6
Few questions:
How do we interpret the
prior causes; how we give
weights to them and their
collateral effects, how we use
and negotiate these causal
explanations?
36
ElectricityFire
Smoke
-
CO2
Alarm
signal
-
Loud
Beeping
Irritation
-
Headache
Call
Firefighters
- Extinguish
it
How to solve the
problem?
IF the problem is
Turn It Off
-
Burn your
soul in hell
Side effects
3636
37. 37 3
7
“A causal structure entails a probability model, but it
contains additional information not contained in the
latter. Causal reasoning [...] denotes the process of
drawing conclusions from a causal model, similar to
the way probability theory allows us to reason about
the outcomes of random experiments. However, since
causal models contain more information than
probabilistic ones do, causal reasoning is more
powerful than probabilistic reasoning, because causal
reasoning allows us to analyze the effect of
interventions or distribution changes.”
3737
38. 3
8
A causal graph is typically
represented as a Directed
Acyclic Graph (DAG), where
the directed edges represent
the direction of causal
influences between variables,
which are represented as
vertices.
3838
More commonly, however, the true data-generating process
is more likely to correspond to a directed acyclic graph (DAG)
model. DAGs do not share the limitations of chain graphs and
have been used for decades to guide inference and modeling,
especially for causal inference (Pearl, 2000).
A sequence of non-repeating vertices (V1, . . . , Vk) is called a path if
for every i = 1, . . . , k − 1, Vi and Vi+1 are connected by an edge.
A path is partially directed if there exists an ordering of the vertices
such that all directed edges in the path point towards the vertex
with a larger index.
A partially directed path is directed if it contains no undirected
edges.
A mixed graph is contains a partially directed cycle if it contains a
partially directed path with a directed edge from the last to the first
node in the path.
A mixed graph with no partially directed cycles is called a chain
graph (CG). A chain graph without undirected edges is called a
directed acyclic graph (DAG), and a chain graph without directed
edges is an undirected graph (UG).
39. But, before it, let’s choose a keyword for some live data mining
39 3
9
Break?
3939
40. Predictive Analysis: Causal Inference
What caused A to agree/disagree with B? Can we build a model to forecast
and retrieve rhetorical behaviors, causal reasoning, and alignments?
40
Descriptive Analysis: LIWC + NLP
Lexical Analysis:
● Linguistic Inquiry Word
Count [LIWC]
● NLTK: Words Count &
Frequency
Semantic Analysis:
● Comparison between text
corpora:
○ Softcossim
○ KL divergence
● Topic Modeling:
○ Latent Semantic
Analysis
○ Latent Dirichlet
Allocation
● Sentiment Analysis
● Word Embeddings:
○ Word2Vec
○ GloVe
○ FastText
● Sentence Embeddings:
○ FastText
○ Doc2Vec
○ Sent2Vec
4
0
Causal Reasoning:
● Structural Equation Models
● Chain Graphs
○ Direct Acyclic Graphs
(DAGs)
Natural Language Understanding:
● CommonSense Inference
(semantic entailment):
○ Event2Mind
○ A TOMIC
○ SWAG
● Reading Comprehension,
Sentence Prediction:
○ Google’s BERT
○ OpenAI’s GPT-2
○ ELMo
4040
41. 41 4
1
Natural Language Understanding (NLU) in NLP is about creating models (e.g. chat-bots)
that, having analyzed huge amounts of text data, may be capable to understand the
semantics of natural language for predicting our linguistic (and semantic) habits.
CommonSense Inference (semantic entailment):
○ Event2Mind
○ A TOMIC
○ SWAG
Reading Comprehension, Sentence Prediction:
○ Google’s BERT
○ OpenAI’s GPT-2
○ ELMo
What is NLU? Which models could be integrate in a ML Pipeline for advancing causal inferences?
Natural Language Inference (NLI) in NLP is
the task of determining whether a
“hypothesis” is true (entailment), false
(contradiction), or undetermined (neutral)
given a “premise”
4141
49. Modeling Online Interaction:
Endogenous factors
● Qualitative Online Discourse
Analysis
● Detect Linguistic & Dialogical
Features
○ Lexical choice
○ Arguments choice
○ Information Contagion
(e.g. URLs, retweets,
mentions)
● Causal disagreement:
○ Linguistic?
○ Cognitive?
49 4
9
It’s about meaning
production per se and
meaning in context
Linguistics
↓
Semantics
↓
Pragmatics
Grammar
↓
Denotation/Connotation
↓
Speech Acts, Context
constraints, etc.
2.
4949
50. 50 5
0
G. Frege - Sense &
Reference, 1892It’s not about language per se, BUT it’s about
how we use language in context:
What’s the reference? What’s the intention?
Reference
(extension,
denotation)
↓
What the
expression
refers to
Sense
(intension,
connotation)
↓
Meaning of the
expression
P.s. Think about Peirce,
Barthes & Eco’s concept of
semiosis.
Think about Pragmatics
5050
51. L. Wittgenstein -
Philosophical
Investigation, 1953
5
1
51 5
1
Meaning is Use: utterances are only
explicable in relation to the activities in
which they play a role; the meaning of a
word is revealed in its use.
He called these activities ‘language-games’.
The rules are learned and made manifest
by actually playing the game.
E. Berne - Games
People Play, 1964
Transactional Analysis: meaning is not set
in stone - does not rely on a prescriptive
level (linguistic or semantic) - but it is
negotiated and constrained by
psychological roles and implicatures that
we consciously and unconsciously embrace
5151
52. 52 5
2
J. Searle - Speech Acts,
1969Not all pseudo-statements are intended
(or only intend in part) to record or impart
straightforward information about some
facts. They are intended to be something
quite different, such as “performative verbs”
e.g. I declare, I christen this, I object, I
sentence, etc.
Illocutionary Act
↓
Act has force in saying
something
Locutionary Act
↓
Act has meaning
Perlocutionary Act
↓
Act as effects
achieving
5252
53. 53 5
3
P. Grice - Maxims,
1975
● Quantity: In answer to "Tell me about him!":
He has a nice personality. [≠ informative]
● Quality: In response to something stupid someone did:
That was brilliant! [≠ true]
● Relation: In response to "Can I go out and play?":
Did you finish your homework? [≠ pertinent]
● Manner: A wedding ring should be tight, after all, it's purpose is
to limit your circulation. [≠ unambiguous]
How do we assess sarcasm,
irony and other weird
psychopathic manipulations ?
5353
56. Modeling Causal
Inference Computationally
Modeling Online Interaction:
Endogenous factors
● Social Media Mining
● Topic Modeling (NLP)
● LIWC Causal Analysis
● Train & Test Classifier
● Implementation for SCM
(Structural Causal Models)
● Qualitative Online Discourse
Analysis
● Detect Linguistic & Dialogical
Features
○ Lexical choice
○ Arguments choice
○ Information Contagion
(e.g. URLs, retweets,
mentions)
● Causal disagreement:
○ Linguistic?
○ Cognitive?
● Social Networks structure, ties,
engagement, news sources
and availability
● Benchmark findings of
Linguistic & Dialogical
Features
● Integration, optimization &
validation of the classifier
Modeling Online Interaction:
Exogenous factors
56 5
6
3.
5656
57. 57 5
7
Visualizing Twitter’s networks
Hoaxy is an open platform developed at
Indiana University to track the spread of
claims and fact checking.
A search engine, interactive visualizations,
and open-source software are freely available
(hoaxy.iuni.iu.edu). The data are accessible
through a public application programme
interface (API).
Enter a keyword, search Twitter content (from the last week) or
Hoaxy, i.e. articles from misinformation and fact-checking source.
You can even select up to 20 related articles and generate a
timeline with a network graph
5757
59. Back to Twitter and Complex Networks: Information Reception
59 5
9
Selective
Exposure is
influenced
by online
behaviour
Recommendation
Systems (search
engines, previous
chronology online)
Homophily
(tendency to group
according to
interests and
commonalities)
Algorithmic bias
Confirmation bias
Filter
Bubbles
Info sources are
constrained
Confirmation bias
consolidated
Info patterns strongly
repeated
Echo -
Chambers
5959
60. 60 6
0
Active shift:
Group Polarization
Echo -
Chambers
Group polarization (C. R. Sunstein, 2002), on a basic level, is that social
tendency of a
“predictable shift within a group discussing a case or a problem. As
the shift occurs, groups, and group members move and coalesce, not
toward the middle of antecedent dispositions, but toward a more
extreme position in the direction indicated by those dispositions.
The effect of deliberation is both to decrease variance among group
members, as individual differences diminish, and also to produce
convergence on a relatively more extreme point among pre-deliberation
judgments”
Back to Twitter and Complex Networks: Information Reception
6060
61. 61 6
1
Active shift:
Group Polarization
Partisan polarization
is common in political
groups
It can boost political
discussions and
engagements
Cross-ideological
exposure mitigate
echo-chambers
Back to Twitter and Complex Networks: Information Reception
On the evidence of cross-ideological political
discourse, Garrett (2009) points out that even if
selective exposure occurs for individuals and online
news, “people do not seek to completely exclude other
perspectives from their political universe, and there is
little evidence that they will use the Internet to create
echo chambers, devoid of other viewpoints, no matter
how much control over their political informative
environment they are given.
To the contrary, the longer read times associated
with opinion-challenging information suggest that
people may wish to maintain awareness of diverse
political views (while ensuring that their own beliefs
are well supported)”.
6161
63. 63 6
3
Cross-ideological
exposure
Given these variables, how we model causal inference?
Selective
Exposure is
influenced
by online
behaviour
Filter
Bubbles
Echo -
Chambers
Active shift:
Group Polarization
Chain Graphs ?
6363
64. Network-oriented
modelling based on
temporal-causal
networks (?)
64 6
4
6464
The Network-Oriented Modelling
approach based on temporal–causal
networks is a generic and declarative
dynamic modelling approach based on
networks of causal relations. Dynamics
is addressed by incorporating a
continuous time dimension.
This temporal dimension enables
modelling by networks that inherently
contain cycles, such as networks
modelling mental or brain processes, or
social interaction processes, and also
enables to address the timing of the
processes in a differentiated manner.
65. 65
Descriptive Analysis: LIWC + NLP
Lexical Analysis:
● Linguistic Inquiry Word
Count [LIWC]
● NLTK: Words Count &
Frequency
Semantic Analysis:
● Comparison between text
corpora:
○ Softcossim
○ KL divergence
● Topic Modeling:
○ Latent Semantic
Analysis
○ Latent Dirichlet
Allocation
● Sentiment Analysis
● Word Embeddings:
○ Word2Vec
○ GloVe
○ FastText
● Sentence Embeddings:
○ FastText
○ Doc2Vec
○ Sent2Vec
Predictive Analysis: Causal Inference
Network Interferences: Information Diffusion & Contagion
Causal Reasoning:
● Structural Equation Models
● Chain Graphs
○ Direct Acyclic Graphs
(DAGs)
Natural Language Understanding:
● CommonSense Inference
(semantic entailment):
○ Event2Mind
○ A TOMIC
○ SWAG
● Reading Comprehension,
Sentence Prediction:
○ Google’s BERT
○ OpenAI’s GPT-2
○ ELMo
Pragmatic Distortion:
○ Linguistic Ambiguity
(e.g. lexical
constraints)
○ Semantic Ambiguity
(e.g. speech acts,
sense and reference,
Implicatures, etc.))
Network Interference:
● Network Structure
○ Social Network Ties
● Engagement
○ Attention
○ Responsiveness
○ Interests
● News Diet
○ News sources
○ News agenda
● Network Exposure
○ Filter Bubbles
○ Echo-Chambers
● Behavioral patterns
○ Selective Exposure
(Homophily)
○ Epistemic Authority
What caused A to agree/disagree with B? Can we build a model to forecast
and retrieve rhetorical behaviors, causal reasoning, and alignments?
What caused A to agree/disagree with B? Can we build a model to forecast and retrieve rhetorical behaviors, causal
reasoning, and alignments quantifying all the network interferences that do shape information diffusion and contagion?
6
5
6565
66. Information Diffusion Information Contagion
News
Qual-Quant
Filter
Bubbles
Engagement
Selective
Exposure
Algorithmic
Bias
Network
Ties
Endogenous & Exogenous variables: a sketch
66 6
6
Network
Structure
Agents’
Interplay
Sources’
Interplay
Complex Networks - Information Studies Cognitive Science
Semiotics - Pragmatics
Chain Graphs/SCMs, NLP/NLU NLP
Semantic
Tendencies
Linguistic
Tendencies
Dialogical
Interplay
Information Environment Information Reception Information TradingInformation Flow
6666
67. Information Diffusion Information Contagion
Future Directions
A pipeline composed by NLU commonsense models, DAGs and other mixed chain
graphs? How to deal with different timescales in a dynamic framework?
How to harness endogenous and exogenous variables?
Endogenous & Exogenous variables: a sketch
67 6
7
Information Environment Information Reception Information TradingInformation Flow
6767
69. 69 6
9
Data Scraping
through the API:
GetOldTweets3
Bonus part: Let’s play around
Data
Preprocessing
Text stripping and
normalization
Data Wrangling
LDA +
FastText
Data
Visualization
NetworkX - users’
interactions
Disclaimer:
I hope that my CPU, Conda,
and Python frameworks will
allow me to do that
Choose a topic and some keywords!
6969
70. 70 7
0
● Peters, J., Janzing, D. and Schölkopf, B., 2017. Elements of causal inference: foundations and learning algorithms. MIT press.
● Christiansen, M.H. and Chater, N., 2016. Creating language: Integrating evolution, acquisition, and processing. MIT Press.
● Hume, D., 2012. A treatise of human nature (1739). Courier Corporation.
● Pearl, J., 2000. Causality: models, reasoning and inference (Vol. 29). Cambridge: MIT press.
● Goodman, N.D., Ullman, T.D. and Tenenbaum, J.B., 2011. Learning a theory of causality. Psychological Review, 118(1), p.110.
● Tylén, K., Weed, E., Wallentin, M., Roepstorff, A. and Frith, C.D., 2010. Language as a tool for interacting minds. Mind & Language, 25(1),
pp.3-29.
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