The document discusses automatically identifying Islamophobia in social media text. It begins by introducing the speaker and their areas of research, including hate speech detection. It then provides background on Islamophobia, discussing its origins and definitions. The remainder of the document outlines a project to collect and annotate Twitter data containing mentions of Ilhan Omar to detect Islamophobic sentiment, discussing the pilot annotation process and lessons learned.
How india muslims are being demonised through whats app groups (a critical studyZahidManiyar
ย
This document summarizes a research paper that analyzes "fear speech" on WhatsApp groups in India. The researchers:
1) Define fear speech and distinguish it from hate speech, finding fear speech aims to instill fear of minority groups through misinformation rather than using toxic language.
2) Create a dataset of over 27,000 WhatsApp posts, manually labeling 8,000 as fear speech and 19,000 as non-fear speech, focusing on Islamophobic fear speech.
3) Develop models to identify fear speech automatically and conduct an online survey to understand the characteristics of users who share and consume fear speech.
Linguistic Cues to Deception: Identifying Political Trolls on Social MediaAseel Addawood
ย
The document summarizes research on identifying political trolls on social media through their linguistic cues of deception. Key findings include:
- Russian trolls active in the 2016 US election aimed to sow discord by discussing divisive topics like Trump, police, and race on Twitter.
- Analyses found trolls used more deceptive language through increased uncertainty, less self-reference, and shorter, less complex tweets than non-trolls.
- Machine learning classifiers could predict trolls with over 80% accuracy based on their deceptive linguistic patterns, showing the potential to automatically detect political trolls online.
Can Artificial Intelligence Predict The Spread Of Online Hate Speech?Bernard Marr
ย
Online hate speech is a big issue, and many are worried that it leads to radicalization and actions in the real world. Here, we look at how artificial intelligence (AI) can now be used to detect hate speech and predict its impact.
The document criticizes modern American conservatism and Republican policies. It argues that Republicans misuse labels like "liberal" and "conservative" to demonize progressive ideology. While both parties serve special interests, liberalism better promotes social responsibility, equality, and progress. The document also lists common conservative rhetorical tactics like blaming the "liberal media" and claims they "represent the middle class." It aims to counter such strategies by educating people about the true meanings and histories of political terms.
This document summarizes an analysis of ISIS Twitter activity. It identifies the problem of ISIS using Twitter to further its agenda and degrade the US, and the hypothesis that analyzing ISIS Twitter data can enhance understanding of their networks and tactics. It then describes collecting data from 10 self-identified jihadist Twitter accounts, performing network and text analytics on the data, and finding two distinct communities centered around British foreign fighters in Syria and information about Syrian Islamist groups. It concludes more data is needed but finds no correlation between US airstrikes and tweet volume except for one user, and that tweets generally contained religious statements or news about fighting in Syria.
MySpace is Your Library's Space: Second Life Presentationsomedayangeline
ย
This document provides an overview of the social networking site MySpace, including its history and uses. It discusses how libraries can create MySpace accounts to connect with users, share resources and events, and discuss issues like privacy, policies, and potential controversies associated with the site. The document also provides examples of how libraries have used MySpace for reference, programming, and promotion.
This document provides an overview of library research for a criminology course. It covers topics such as determining authority, principles of good searching, finding articles and statistics, and citing sources. The document includes outlines, screenshots, and images to illustrate key points. Research strategies are discussed, such as developing a research question, identifying key concepts, using appropriate keywords and Boolean operators to search databases. Tips are also provided for evaluating sources and determining if information is data or statistics.
This document provides instructions for writing an I-Search paper, which involves researching a topic of interest. The paper has three sections: the story of the search process, what was learned, and reflections on the search. Students are guided to keep a search journal documenting their research steps and progress. They are also instructed on how to evaluate sources, take notes on source cards, and create a works cited page in MLA format.
How india muslims are being demonised through whats app groups (a critical studyZahidManiyar
ย
This document summarizes a research paper that analyzes "fear speech" on WhatsApp groups in India. The researchers:
1) Define fear speech and distinguish it from hate speech, finding fear speech aims to instill fear of minority groups through misinformation rather than using toxic language.
2) Create a dataset of over 27,000 WhatsApp posts, manually labeling 8,000 as fear speech and 19,000 as non-fear speech, focusing on Islamophobic fear speech.
3) Develop models to identify fear speech automatically and conduct an online survey to understand the characteristics of users who share and consume fear speech.
Linguistic Cues to Deception: Identifying Political Trolls on Social MediaAseel Addawood
ย
The document summarizes research on identifying political trolls on social media through their linguistic cues of deception. Key findings include:
- Russian trolls active in the 2016 US election aimed to sow discord by discussing divisive topics like Trump, police, and race on Twitter.
- Analyses found trolls used more deceptive language through increased uncertainty, less self-reference, and shorter, less complex tweets than non-trolls.
- Machine learning classifiers could predict trolls with over 80% accuracy based on their deceptive linguistic patterns, showing the potential to automatically detect political trolls online.
Can Artificial Intelligence Predict The Spread Of Online Hate Speech?Bernard Marr
ย
Online hate speech is a big issue, and many are worried that it leads to radicalization and actions in the real world. Here, we look at how artificial intelligence (AI) can now be used to detect hate speech and predict its impact.
The document criticizes modern American conservatism and Republican policies. It argues that Republicans misuse labels like "liberal" and "conservative" to demonize progressive ideology. While both parties serve special interests, liberalism better promotes social responsibility, equality, and progress. The document also lists common conservative rhetorical tactics like blaming the "liberal media" and claims they "represent the middle class." It aims to counter such strategies by educating people about the true meanings and histories of political terms.
This document summarizes an analysis of ISIS Twitter activity. It identifies the problem of ISIS using Twitter to further its agenda and degrade the US, and the hypothesis that analyzing ISIS Twitter data can enhance understanding of their networks and tactics. It then describes collecting data from 10 self-identified jihadist Twitter accounts, performing network and text analytics on the data, and finding two distinct communities centered around British foreign fighters in Syria and information about Syrian Islamist groups. It concludes more data is needed but finds no correlation between US airstrikes and tweet volume except for one user, and that tweets generally contained religious statements or news about fighting in Syria.
MySpace is Your Library's Space: Second Life Presentationsomedayangeline
ย
This document provides an overview of the social networking site MySpace, including its history and uses. It discusses how libraries can create MySpace accounts to connect with users, share resources and events, and discuss issues like privacy, policies, and potential controversies associated with the site. The document also provides examples of how libraries have used MySpace for reference, programming, and promotion.
This document provides an overview of library research for a criminology course. It covers topics such as determining authority, principles of good searching, finding articles and statistics, and citing sources. The document includes outlines, screenshots, and images to illustrate key points. Research strategies are discussed, such as developing a research question, identifying key concepts, using appropriate keywords and Boolean operators to search databases. Tips are also provided for evaluating sources and determining if information is data or statistics.
This document provides instructions for writing an I-Search paper, which involves researching a topic of interest. The paper has three sections: the story of the search process, what was learned, and reflections on the search. Students are guided to keep a search journal documenting their research steps and progress. They are also instructed on how to evaluate sources, take notes on source cards, and create a works cited page in MLA format.
Social media is a very powerful tool and like any other tool (knife, axe, etc) its impact depends on the intention of its user. Go through this slideshow to reflect back on what we had learnt from social media and its use in politics across countries from 2014 to 2018.
This document provides guidance on effectively researching and using sources for a Year 9 history assignment on slavery in ancient Greece. It discusses the information process, available resources on the library website, and strategies for evaluating, selecting, reading and comparing both primary and secondary sources. Students are instructed on differentiating between primary and secondary sources, given examples of each, and engaged in partner activities to practice evaluating websites, reading digital texts, and corroborating information across multiple sources.
The document provides information about the satirical Facebook page "Humans of Hindutva". It began in April 2017 and has gained over 100,000 likes in under 7 months. The anonymous page parodies right-wing views in India and was created as an alternative to political satire publications abroad. It aims to highlight issues like casteism, moral policing, and attacks by cow protection groups through humor and satire. While it resonates with liberal audiences, it also faces threats and legal challenges due to its controversial content. The page leverages Facebook and other online platforms to build a counter-culture community, though it must also contend with censorship and the lack of protections for online satirists in India.
The document discusses internet monitoring and identifying user opinions from text. It describes building resources for sentiment analysis in Romanian and experiments on classifying opinions on several topics from Romanian texts with around 44% accuracy. A case study on wiretapping opinions found more negative than positive views, focused on recent laws and the Romanian intelligence agency.
This paper examines the issue of downloading music for free illegally. It summarizes two journal articles that studied how anticipated guilt and personal ethics influence people's decisions to illegally download music. The first article found that people with higher anticipated guilt were less likely to download illegally again. The second article found that illegal downloaders had lower ethical concerns and felt downloading did not harm musicians or companies. The paper also discusses how the Marshall University library provides valuable free resources that save students money compared to other options for research.
This paper examines the issue of downloading music for free illegally. It summarizes two journal articles that studied how anticipated guilt and personal ethics influence people's decisions to illegally download music. The first article found that people with higher anticipated guilt were less likely to download illegally again. The second article found that illegal downloaders had lower ethical concerns and felt downloading did not harm musicians or companies. The paper also discusses how the Marshall University library provides valuable free resources like databases and interlibrary loans that help students research topics and save money compared to other options.
This document summarizes a study that analyzed the Twitter usage of members of the 114th US Congress. The study found:
1) Members of both parties used hashtags to communicate about issues, both those their party "owned" and those the other party owned, showing willingness to discuss various issues.
2) For common hashtags like #immigration or #Obamacare, both Democratic and Republican usage was influenced by ideological factors and constituency characteristics, though the relative influence of each varied by issue.
3) While parties often used different hashtags for the same issue, there was significant overlap in the types of issues each party communicated about via Twitter.
This document provides guidance for Year 9 history students on conducting research for an assignment using the library website and other resources. It discusses the information process, locating and selecting primary and secondary sources, evaluating websites, and strategies for reading and analyzing sources. Students are instructed to use multiple sources, consider author credibility and date of publication, and look for corroboration across sources. The lesson includes individual and group activities to practice these skills by analyzing example websites on ancient Greek slavery.
This document summarizes research on predicting political ideology from Twitter text. It finds that:
1. Previous studies likely oversimplified the problem by using non-representative samples of highly politically engaged users, when political ideology exists on a spectrum.
2. Predicting specific positions (e.g. conservative vs. liberal) achieves high accuracy, but accuracy drops significantly for finer-grained predictions (e.g. extreme vs. moderate).
3. Language analysis can distinguish neutral, moderate, and extreme users, suggesting political engagement is a separate dimension from ideology.
4. A new dataset and modeling approach are needed to more fully capture political ideology and engagement as continuous variables.
This document summarizes research on using machine learning to build an ideologically balanced news diet. The researchers trained classification models on debate transcripts to predict whether news articles came from left-leaning or right-leaning media sources. The models achieved 84% accuracy but predicted that 79% of articles were from right-leaning sources, which did not match other data. The researchers discuss potential reasons for this and ways to improve the models in future iterations, such as using more training data sources and articles to better represent the ideological spectrum.
Kurnava_CyberStalking Vulnerability Research PaperMatthew Kurnava
ย
This document summarizes a research paper on how advancements in technology have enhanced the ability to cyberstalk individuals. It begins with an introduction discussing a 1999 murder case where the perpetrator was able to track and kill the victim after becoming obsessed with her online. The paper then outlines the research question of how technology has allowed access to personal information and enabled stalking. It presents a hypothesis that technological advancements have increased capabilities but also privacy risks. The literature review discusses definitions of cyberstalking, laws, statistics on its prevalence, and a typology of cyber stalker behaviors. The methodology will qualitatively study how vulnerable a volunteer is to cyberstalking using different technologies like smartphones and computers.
Hate speech is language intended to cause harm against a particular individual or group, often based on their racial, ethnic, religious, or gender identity. Hate speech is widespread on social media, and is increasingly common in mainstream political discourse. That said, there is no clear consensus as to what constitutes hate speech. In addition, human moderators come with their own biases, and automatic computer algorithms are often easy to fool. All of these factors complicate the efforts of social media platforms to filter or reduce such content. During this interactive workshop we will discuss examples from Twitter in the hopes of reaching some consensus as to what is and is not hate speech. We will also try to determine what kind of knowledge a human moderator or an automatic algorithm would need to have in order to make this determination. We will try to avoid particularly graphic examples of hate speech and focus on more subtle cases.
Slides for Muslims in ML workshop presentation at NeurlPS 2020 on December 8, 2020 - this is a shorter 25 minute version of the UMass Lowell talk of November 2020 (so the slides are a subset of that).
250 word discussion response james wk 7.docxwrite12
ย
1) The internet has allowed potential terrorists to easily access information and communicate remotely, making lone wolf radicalization and attacks more difficult to detect.
2) Social media platforms removing extremist content limits intelligence gathering opportunities for law enforcement.
3) Increased public education and community policing can help identify lone wolf threats by encouraging people to report suspicious activity. Close cooperation between law enforcement and communities is important for prevention.
SUPER-FINAL-PPT_SMISHING.pptx Stop the smishing: A pragmatic Analysis on Dece...ElmeBaje
ย
This research proposal aims to analyze deceptive text messages ("smishing") and identify language patterns used. The study will determine which age and gender groups receive the most messages, how scammers persuade victims, and the most common types of fraud messages. Using speech act theory, the researchers will analyze 100 scam messages from Iligan City residents to identify recurring words, illocutionary acts, and the impact on receivers. Content analysis and interviews will be used to understand how scammers deceive people through language to obtain private information. The results could help prevent people from falling victim to fraudulent messages.
Understanding Online Socials Harm: Examples of Harassment and RadicalizationAmit Sheth
ย
https://dbsec2019.cse.sc.edu/Keynote.html
Abstract: As social media permeates our daily life, there has been a sharp rise in the misuse of social media affecting our society in large. Specifically, harassment and radicalization have become two major problems on social media platforms with significant implications on the well-being of individuals as well as communities. A 2017 Pew Research survey on online harassment found that 66% of adult Internet users have observed online harassment and 41% have personally experienced it. Nearly 18% of Americans have faced severe forms of harassment online such as physical threats, harassment over a sustained period, sexual harassment or stalking. Moreover, malicious organizations (e.g., terrorist groups, white nationalists not classified legally as terrorists but as a group with extreme ideology) have been using social media for sharing their propaganda and misinformation to persuade individuals and eventually recruit them to propagate their ideology. These communications related to harassment and radicalization are complex concerning their language and contextual characteristics, making recognition of such narratives challenging for researchers as well as social media companies. As most of the existing approaches fail to capture fundamental nuances in the language of these communications, two prominent challenges have emerged: ambiguity and sparsity. Sole data level bottom-up analysis has been unsuccessful in revealing the actual meaning of the content. Considering the significant sensitivity of these problems and its implications at individual and community levels, a potential solution requires reliable algorithms for modeling such communications.
Our approach to understanding communications between source and target requires deciphering the unique language, semantic and contextual characteristics, including sentiment, emotion, and intention. This context-aware and knowledge-enhanced computational approach to the analysis of these narratives breaks down this long-running and complex process into contextual building blocks that acknowledge inherent ambiguity and sparsity. Based on prior empirical and qualitative research in social sciences, particularly cognitive psychology, and political science, we model this process using a combination of contextual dimensions -- e.g., for Islamist radicalization: religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible.
Malaria. Malaria: Causes, Symptoms, and Diagnosis Essay Example | Topics and .... Essay on Malaria. Outbreak of Malaria in the US Essay Example | Topics and Well Written .... Malaria An Essay. (PDF) True malaria prevalence in children under five: Bayesian .... Malaria - Essay by Kbrother2006 - Anti Essays. Disease Control and Prevention: Malaria Essay Example | Topics and Well .... Malaria: An Essay on the Production and Propagation of this Poison, and .... Malaria - English โ Depicta. Malaria essay medicine buy custom written malaria essay. MALARIA. Tracking Malaria With Amy Maxmen | Pulitzer Center. แ Essays On Malaria
The document discusses the role of public libraries in promoting information literacy. It defines information literacy as the ability to use various resources and literacies to find information, as per the American Association of School Libraries' standards. These standards cover skills like critical thinking, drawing conclusions, and applying knowledge. The document states that public libraries provide important information literacy programs and support lifelong learning in their communities by reaching a diverse range of people.
The document discusses the topic of hate crimes. It begins by defining hate crimes as violent acts against people or organizations because of the group they belong to. It then provides examples of hate crimes targeting different groups such as Jews, Muslims, and women. The document discusses how hate crimes are often committed by ordinary people and explores the psychology of hate crime perpetrators. It also looks at debates around hate crime laws and efforts to record and address hate crimes.
This document analyzes the impoliteness strategies used by the Clinton and Trump presidential campaigns on Twitter during the 2016 US election. It finds that Trump's campaign frequently used bald-on-record impoliteness like name-calling and insults, while Clinton's campaign more often employed positive impoliteness like questioning Trump's abilities or experience. Both campaigns reacted to campaign events by increasing impolite tweets. The study analyzed over 12,000 tweets using frameworks of conventional impoliteness and strategies from Culpeper to understand differences in how each campaign communicated (im)politeness online.
The document discusses the steps involved in requesting and receiving a custom written paper from the website HelpWriting.net. It outlines registering for an account, completing an order form with instructions and deadlines, and utilizing a bidding system where writers submit proposals and the client selects a writer to complete the paper. The process concludes with the client receiving the paper, reviewing it for quality, and authorizing payment if satisfied with the work.
Social media is a very powerful tool and like any other tool (knife, axe, etc) its impact depends on the intention of its user. Go through this slideshow to reflect back on what we had learnt from social media and its use in politics across countries from 2014 to 2018.
This document provides guidance on effectively researching and using sources for a Year 9 history assignment on slavery in ancient Greece. It discusses the information process, available resources on the library website, and strategies for evaluating, selecting, reading and comparing both primary and secondary sources. Students are instructed on differentiating between primary and secondary sources, given examples of each, and engaged in partner activities to practice evaluating websites, reading digital texts, and corroborating information across multiple sources.
The document provides information about the satirical Facebook page "Humans of Hindutva". It began in April 2017 and has gained over 100,000 likes in under 7 months. The anonymous page parodies right-wing views in India and was created as an alternative to political satire publications abroad. It aims to highlight issues like casteism, moral policing, and attacks by cow protection groups through humor and satire. While it resonates with liberal audiences, it also faces threats and legal challenges due to its controversial content. The page leverages Facebook and other online platforms to build a counter-culture community, though it must also contend with censorship and the lack of protections for online satirists in India.
The document discusses internet monitoring and identifying user opinions from text. It describes building resources for sentiment analysis in Romanian and experiments on classifying opinions on several topics from Romanian texts with around 44% accuracy. A case study on wiretapping opinions found more negative than positive views, focused on recent laws and the Romanian intelligence agency.
This paper examines the issue of downloading music for free illegally. It summarizes two journal articles that studied how anticipated guilt and personal ethics influence people's decisions to illegally download music. The first article found that people with higher anticipated guilt were less likely to download illegally again. The second article found that illegal downloaders had lower ethical concerns and felt downloading did not harm musicians or companies. The paper also discusses how the Marshall University library provides valuable free resources that save students money compared to other options for research.
This paper examines the issue of downloading music for free illegally. It summarizes two journal articles that studied how anticipated guilt and personal ethics influence people's decisions to illegally download music. The first article found that people with higher anticipated guilt were less likely to download illegally again. The second article found that illegal downloaders had lower ethical concerns and felt downloading did not harm musicians or companies. The paper also discusses how the Marshall University library provides valuable free resources like databases and interlibrary loans that help students research topics and save money compared to other options.
This document summarizes a study that analyzed the Twitter usage of members of the 114th US Congress. The study found:
1) Members of both parties used hashtags to communicate about issues, both those their party "owned" and those the other party owned, showing willingness to discuss various issues.
2) For common hashtags like #immigration or #Obamacare, both Democratic and Republican usage was influenced by ideological factors and constituency characteristics, though the relative influence of each varied by issue.
3) While parties often used different hashtags for the same issue, there was significant overlap in the types of issues each party communicated about via Twitter.
This document provides guidance for Year 9 history students on conducting research for an assignment using the library website and other resources. It discusses the information process, locating and selecting primary and secondary sources, evaluating websites, and strategies for reading and analyzing sources. Students are instructed to use multiple sources, consider author credibility and date of publication, and look for corroboration across sources. The lesson includes individual and group activities to practice these skills by analyzing example websites on ancient Greek slavery.
This document summarizes research on predicting political ideology from Twitter text. It finds that:
1. Previous studies likely oversimplified the problem by using non-representative samples of highly politically engaged users, when political ideology exists on a spectrum.
2. Predicting specific positions (e.g. conservative vs. liberal) achieves high accuracy, but accuracy drops significantly for finer-grained predictions (e.g. extreme vs. moderate).
3. Language analysis can distinguish neutral, moderate, and extreme users, suggesting political engagement is a separate dimension from ideology.
4. A new dataset and modeling approach are needed to more fully capture political ideology and engagement as continuous variables.
This document summarizes research on using machine learning to build an ideologically balanced news diet. The researchers trained classification models on debate transcripts to predict whether news articles came from left-leaning or right-leaning media sources. The models achieved 84% accuracy but predicted that 79% of articles were from right-leaning sources, which did not match other data. The researchers discuss potential reasons for this and ways to improve the models in future iterations, such as using more training data sources and articles to better represent the ideological spectrum.
Kurnava_CyberStalking Vulnerability Research PaperMatthew Kurnava
ย
This document summarizes a research paper on how advancements in technology have enhanced the ability to cyberstalk individuals. It begins with an introduction discussing a 1999 murder case where the perpetrator was able to track and kill the victim after becoming obsessed with her online. The paper then outlines the research question of how technology has allowed access to personal information and enabled stalking. It presents a hypothesis that technological advancements have increased capabilities but also privacy risks. The literature review discusses definitions of cyberstalking, laws, statistics on its prevalence, and a typology of cyber stalker behaviors. The methodology will qualitatively study how vulnerable a volunteer is to cyberstalking using different technologies like smartphones and computers.
Hate speech is language intended to cause harm against a particular individual or group, often based on their racial, ethnic, religious, or gender identity. Hate speech is widespread on social media, and is increasingly common in mainstream political discourse. That said, there is no clear consensus as to what constitutes hate speech. In addition, human moderators come with their own biases, and automatic computer algorithms are often easy to fool. All of these factors complicate the efforts of social media platforms to filter or reduce such content. During this interactive workshop we will discuss examples from Twitter in the hopes of reaching some consensus as to what is and is not hate speech. We will also try to determine what kind of knowledge a human moderator or an automatic algorithm would need to have in order to make this determination. We will try to avoid particularly graphic examples of hate speech and focus on more subtle cases.
Slides for Muslims in ML workshop presentation at NeurlPS 2020 on December 8, 2020 - this is a shorter 25 minute version of the UMass Lowell talk of November 2020 (so the slides are a subset of that).
250 word discussion response james wk 7.docxwrite12
ย
1) The internet has allowed potential terrorists to easily access information and communicate remotely, making lone wolf radicalization and attacks more difficult to detect.
2) Social media platforms removing extremist content limits intelligence gathering opportunities for law enforcement.
3) Increased public education and community policing can help identify lone wolf threats by encouraging people to report suspicious activity. Close cooperation between law enforcement and communities is important for prevention.
SUPER-FINAL-PPT_SMISHING.pptx Stop the smishing: A pragmatic Analysis on Dece...ElmeBaje
ย
This research proposal aims to analyze deceptive text messages ("smishing") and identify language patterns used. The study will determine which age and gender groups receive the most messages, how scammers persuade victims, and the most common types of fraud messages. Using speech act theory, the researchers will analyze 100 scam messages from Iligan City residents to identify recurring words, illocutionary acts, and the impact on receivers. Content analysis and interviews will be used to understand how scammers deceive people through language to obtain private information. The results could help prevent people from falling victim to fraudulent messages.
Understanding Online Socials Harm: Examples of Harassment and RadicalizationAmit Sheth
ย
https://dbsec2019.cse.sc.edu/Keynote.html
Abstract: As social media permeates our daily life, there has been a sharp rise in the misuse of social media affecting our society in large. Specifically, harassment and radicalization have become two major problems on social media platforms with significant implications on the well-being of individuals as well as communities. A 2017 Pew Research survey on online harassment found that 66% of adult Internet users have observed online harassment and 41% have personally experienced it. Nearly 18% of Americans have faced severe forms of harassment online such as physical threats, harassment over a sustained period, sexual harassment or stalking. Moreover, malicious organizations (e.g., terrorist groups, white nationalists not classified legally as terrorists but as a group with extreme ideology) have been using social media for sharing their propaganda and misinformation to persuade individuals and eventually recruit them to propagate their ideology. These communications related to harassment and radicalization are complex concerning their language and contextual characteristics, making recognition of such narratives challenging for researchers as well as social media companies. As most of the existing approaches fail to capture fundamental nuances in the language of these communications, two prominent challenges have emerged: ambiguity and sparsity. Sole data level bottom-up analysis has been unsuccessful in revealing the actual meaning of the content. Considering the significant sensitivity of these problems and its implications at individual and community levels, a potential solution requires reliable algorithms for modeling such communications.
Our approach to understanding communications between source and target requires deciphering the unique language, semantic and contextual characteristics, including sentiment, emotion, and intention. This context-aware and knowledge-enhanced computational approach to the analysis of these narratives breaks down this long-running and complex process into contextual building blocks that acknowledge inherent ambiguity and sparsity. Based on prior empirical and qualitative research in social sciences, particularly cognitive psychology, and political science, we model this process using a combination of contextual dimensions -- e.g., for Islamist radicalization: religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible.
Malaria. Malaria: Causes, Symptoms, and Diagnosis Essay Example | Topics and .... Essay on Malaria. Outbreak of Malaria in the US Essay Example | Topics and Well Written .... Malaria An Essay. (PDF) True malaria prevalence in children under five: Bayesian .... Malaria - Essay by Kbrother2006 - Anti Essays. Disease Control and Prevention: Malaria Essay Example | Topics and Well .... Malaria: An Essay on the Production and Propagation of this Poison, and .... Malaria - English โ Depicta. Malaria essay medicine buy custom written malaria essay. MALARIA. Tracking Malaria With Amy Maxmen | Pulitzer Center. แ Essays On Malaria
The document discusses the role of public libraries in promoting information literacy. It defines information literacy as the ability to use various resources and literacies to find information, as per the American Association of School Libraries' standards. These standards cover skills like critical thinking, drawing conclusions, and applying knowledge. The document states that public libraries provide important information literacy programs and support lifelong learning in their communities by reaching a diverse range of people.
The document discusses the topic of hate crimes. It begins by defining hate crimes as violent acts against people or organizations because of the group they belong to. It then provides examples of hate crimes targeting different groups such as Jews, Muslims, and women. The document discusses how hate crimes are often committed by ordinary people and explores the psychology of hate crime perpetrators. It also looks at debates around hate crime laws and efforts to record and address hate crimes.
This document analyzes the impoliteness strategies used by the Clinton and Trump presidential campaigns on Twitter during the 2016 US election. It finds that Trump's campaign frequently used bald-on-record impoliteness like name-calling and insults, while Clinton's campaign more often employed positive impoliteness like questioning Trump's abilities or experience. Both campaigns reacted to campaign events by increasing impolite tweets. The study analyzed over 12,000 tweets using frameworks of conventional impoliteness and strategies from Culpeper to understand differences in how each campaign communicated (im)politeness online.
The document discusses the steps involved in requesting and receiving a custom written paper from the website HelpWriting.net. It outlines registering for an account, completing an order form with instructions and deadlines, and utilizing a bidding system where writers submit proposals and the client selects a writer to complete the paper. The process concludes with the client receiving the paper, reviewing it for quality, and authorizing payment if satisfied with the work.
The document discusses racism in schools and its impact on children. It argues that while children may not fully understand racism at a young age, it is important to teach them about the hard facts of racism in America's history so they do not believe the country is inherently good. Learning about racism can help children avoid repeating the same mistakes of the past and show them that stereotypes and slurs can be emotionally harmful. It also discusses how racism operates through implicit biases and policies that favor white people, and how this cycle of dehumanization must be addressed through education.
The document provides instructions for requesting and completing an assignment writing request on the HelpWriting.net site. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and choose one based on qualifications. 4) Review the completed paper and authorize payment if satisfied. 5) Request revisions to ensure satisfaction and receive a refund if plagiarized.
Similar to Automatically Identifying Islamophobia in Social Media (13)
Talk on Algorithmic Bias given at York University (Canada) on March 11, 2019. This is a shorter version of an interactive workshop presented at University of Minnesota, Duluth in Feb 2019.
The document discusses the history and evolution of dictionaries from the first English dictionary in 1604 to modern computational approaches using natural language processing. It describes early dictionaries like Robert Cawdrey's Table Alphabeticall and Samuel Johnson's A Dictionary of the English Language. Later influential dictionaries included Noah Webster's American Dictionary of the English Language and the Oxford English Dictionary. The document proposes that natural language processing techniques like analyzing word frequencies, collocations, and measures of association could help identify emerging words and senses in new text, similar to the work of lexicographers in compiling dictionaries.
The document summarizes research on using lexical decision lists to screen Twitter users for depression and PTSD. It finds that a simple machine learning method using n-grams of varying length up to 6 words and binary weighting achieved the best results. Emoticons and emojis were strong indicators. The top features indicating depression included terms expressing sadness, while PTSD indicators included abbreviations and URLs. It suggests self-reporting of conditions may indicate something else requiring discussion.
Poster presented at the Semeval 2015 workshop. Our system clustered words based on their contexts in order to identify their underlying meanings or senses.
This document provides an overview of what it would be like to complete a Master's thesis under Dr. Ted Pedersen. It discusses that research involves asking interesting questions about the world and conducting experiments to answer those questions. Dr. Pedersen's research interests include natural language processing tasks like word sense disambiguation, semantic similarity, and collocation discovery. To succeed, a student needs enthusiasm for research, strong writing skills, and the ability to work independently while communicating regularly with Dr. Pedersen. Previous students have explored various NLP topics and many have gone on to PhD programs. The reading provided is intended to assess the student's understanding and interest in Dr. Pedersen's research areas.
This document summarizes a tutorial on measuring the similarity and relatedness of concepts. It discusses the distinction between semantic similarity and relatedness. It describes several common measures of similarity that use information from ontologies, such as path-based measures, measures that incorporate path and depth, and measures that incorporate information content. It also discusses measures of relatedness that can be used for concepts that are not connected by ontological relations, such as definition-based measures and measures based on gloss vectors constructed from corpus data. Experimental results generally show that gloss vector measures perform best, followed by definition-based measures, with path-based measures performing the worst.
Some thoughts on what it's like to do a Master's thesis with me, including general ideas about research, my research interests, and a few suggestions as to what will lead to success
This document describes UMLS::Similarity, an open source software that measures the semantic similarity or relatedness of biomedical terms from the Unified Medical Language Systems (UMLS). It provides several measures to quantify similarity/relatedness based on the hierarchical structure and definitions of terms in the UMLS. The software can be used via command line, API, or web interface and has been used in applications like word sense disambiguation.
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- Some days may require more work or less sleep, demanding flexibility in your approach.
- The key is to be mindful of your time allocation and strive for a healthy balance across the three categories.
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Lesson Outcomes:
- students will be able to identify and name various types of ornamental plants commonly used in landscaping and decoration, classifying them based on their characteristics such as foliage, flowering, and growth habits. They will understand the ecological, aesthetic, and economic benefits of ornamental plants, including their roles in improving air quality, providing habitats for wildlife, and enhancing the visual appeal of environments. Additionally, students will demonstrate knowledge of the basic requirements for growing ornamental plants, ensuring they can effectively cultivate and maintain these plants in various settings.
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Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐ซ๐๐ฅ๐ข๐๐๐ฅ๐ ๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐จ๐ง ๐ญ๐ก๐ ๐ข๐ง๐ญ๐๐ซ๐ง๐๐ญ:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
Creation or Update of a Mandatory Field is Not Set in Odoo 17
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Automatically Identifying Islamophobia in Social Media
1. Automatically Identifying Islamophobia
in Social Media
Ted Pedersen
Department of Computer Science
University of Minnesota, Duluth
tpederse@umn.edu
@SeeTedTalk
http://www.d.umn.edu/~tpederse
2. My NLP Areas
Word Sense Disambiguation and Discrimination
Semantic Similarity and Relatedness
Text Classification / Sentiment Analysis
Humor Recognition
Hate Speech Detection (Words lead to deeds)
Islamophobia and NLP blog
Ethics and NLP
3. Todayโs Agenda
Islamophobia in General
Islamophobia in Minnesota
Connections to Hate Speech Detection
Collecting and Annotating Twitter Data
Lessons Learned & the Way Forward
4. Islamophobia
A legacy of colonial histories,
particularly those that view the
Muslim world as exotic, savage,
dangerous, and/or desperate for
a โClash of Civlilizations.โ
Orientalism (Said, 1978)
5. Islamophobia
A recent term for an older phenomena
Runnymede Trust (1997, 2017)
Unfounded hostility towards Islam
Practical consequences of such hostility
in unfair discrimination against Muslim
individuals and communities
Exclusion of Muslims from mainstream
political and social affairs.
6. Islamophobia
Anti-Muslim racism
Are Muslims a race? No.
Race is constructed, marginalized groups are often racialized and thereby
assumed to share certain inherent features (often seen as limitations)
โRacism is the fatal coupling of power and difference that creates a
vulnerability to premature death.โ Ruha Benjamin / Ruth Wilson Gilmore
Intersection of religion, ethnicity, race, gender, immigration status, โฆ
7. Common Anti-Muslim Tropes (Bridge Institute)
Islam and Muslims are inherently
violent.
Islam and Muslims are oppresive
to women.
Islam and Muslims are intolerant
towards other religions.
Islam is a political ideology, not
a religion.
In the West, Mulims are using
non-violent stealth jihad to
implement Sharia Law.
Islam is foreign, medieval, and
at odds with Western modernity.
Islam is a monolith.
All Muslims are Arab or Brown.
14. Goal : Identify Islamophobia in Written Text
Why?
Relatively understudied form of Hate Speech in NLP.
Highly intersectional problem since Muslim identity is multi-faceted.
Significant influence on events in the World, the USA, and Minnesota.
How?
Use ideas from NLP, especially Hate Speech Detection.
Create annotated corpora in order to understand problem better, and then
apply Machine Learning or Deep Learning.
15. What is Hate Speech?
It exists along a spectrum of language :
Ordinary โฆ Offensive ... Hate Speech ... Dangerous Speech
It takes many forms :
Insults, Profanity, Bullying, Harassment, Attacks, Abuse, Threats, โฆ
It has many targets :
Racism, Sexism, Anti-Semitism, Islamophobia, โฆ
Hate speech seeks to deny a person or group the right to exist in the future.
16. Growth of Hate Speech Detection I
The detection of offensive and abusive language and hate speech is a problem of
growing interest. Work began to really accelerate around 2015, perhaps given the
increasingly apparent problem that exists on social media platforms.
Abusive Language Workshop (2017, 2018, 2019), as of 2020 now known as
WOAH : Workshop on Online Abuse and Harms (Nov 20, EMNLP)
OffensEval (2019, 2020) Detecting Offensive Language (Dec 12-13, COLING)
SemEval 2019 Detecting Hate Speech Against Women and Immigrants
PAN 2021 Profiling Hate Speech Spreaders on Twitter (upcoming)
17. Growth of Hate Speech Detection II
Schmidt and Wiegand (2017) : A Survey on Hate Speech Detection using Natural
Language Processing, SocialNLP
Fortuna and Nunes (2018) : A Survey on Automatic Detection of Hate Speech in
Text, ACM Computing Surveys
Alan Turing Institute Hate Speech Hub and Reading List for Online Hate & Abuse
Hate Speech Data : more than 60 hate speech data sets in 15 languages
Workshops, shared tasks, survey papers, resources are all very positive. But โฆ
18. Limitations of Hate Speech Detection I
The rise of shared tasks and data sets makes it possible to work on Hate Speech
without thinking too hard about the problem or the people. Itโs all just data.
Keyword detection is easy to game and prone to false negatives / positives.
Grรถndahl et al. 2018, All You Need is "Love": Evading Hate Speech Detection
There is no standard set of classes in which to categorize hate speech. Offensive,
profane, targeted, abusive, racist, threatening, strong, weak, implicit, explicit โฆ
Hate Speech varies depending on the target, the speaker, and local context.
19. Limitations of Hate Speech Detection II
Low Annotator Agreement :
Ross et al (2016) : Measuring the Reliability of Hate Speech Annotations: The
Case of the European Refugee Crisis, NLP4CMC
Waseem et al (2016) : Are You a Racist or Am I Seeing Things? Annotator
Influence on Hate Speech Detection on Twitter. NLP+CSS.
Racial Bias :
Sap et al (2019) : The Risk of Racial Bias in Hate Speech Detection. ACL
Davidson et al (2019) : Racial Bias in Hate Speech and Abusive Language
Detection Datasets. ALW.
20. The Way Forward
Hate Speech Detection is not Just Another Classification Task. Seek out domain
expertise, build relationships, donโt reduce the problem to a data set.
Frey et al. (2018). Artificial Intelligence and Inclusion: Formerly Gang-Involved
Youth as Domain Experts for Analyzing Unstructured Twitter Data. Social
Science Computer Review.
Creating annotated data is likely necessary. Be careful to fully document the
decisions made along the way, paying special attention to annotator background.
Bender and Friedman (2018) Data Statements for NLP : Toward Mitigating
Systems Bias and Enabling Better Science. TACL.
21. How Can We Detect Islamophobia (with NLP)?
Carry out a Qualitative Analysis of text with input from domain experts.
Collect and annotate Tweets.
Seek out a diverse pool of annotators.
Develop annotation scheme / code book.
Be Iterative.
Carry out Quantitative Analysis using Machine Learning or Deep Learning.
22. Data Collection
Islamophobia is global, but has many local variations each with their own issues,
terminology, and ways of being expressed.
This suggests the need for the data to have a regional focus - Islamophobia in the
UK, France, India, the USA, Minnesota, etc.
While she is a national figure, Ilhan Omar is from Minnesota, and our data
collection starts with her.
Muslim, but also a black Somali woman who was an immigrant / refugee.
Highly intersectional identity.
23. Tweet Collection (using Twitter public API)
Collecting since April 2019, any tweet that includes one or more of :
โIlhan omarโ, ilhan, omar, @ilhanmn, ilhanmn, #ilhanmn, #ilhanomar, #ilhan
Pilot Annotation based on April 2019 - April 2020, approx 5 million total tweets.
1020 Annotation based on Nov. 2019 - Oct. 2020, approx 10 million total tweets.
Twitter public API does not give you all tweets, downsamples.
24. Pilot Annotation
Data Statement Ilhan Omar
Islamophobia Data Set, created
during LREC 2020 Data Statements
workshop (May 11-13, 2020)
~5 million tweets from April 2019 -
April 2020. Selected those with
muslim, islam, quran, or koran
(220,000). Drew random subsets of
100 for pilot annotation. Low
annotator agreement.
Traitor/Not Loyal - Muslims are not loyal
and and beholden to some external
organization or government (potential
overlap with Terrorist, Sharia Law)
Terrorist/Sympathizer - Muslims are either
terrorists themselves or support those who
are. (potential overlap with Traitor)
False Religion - Islam is a false religion with
strange, primitive, evil practices.
Sharia Law - Muslims want to replace the
existing legal system with Sharia Law.
(potential overlap with Traitor).
25. 1020 Annotation (October 2020)
9.6 million tweets (incl. RT) collected Nov 2019 - Oct 2020.
1 million unique tweets.
Selected random samples of 384 tweets for annotation.
Agreement improved with more extensive set of labels.
Began to consider profile descriptions of โspeakersโ (Tweeters).
26. 1020 Annotation Labels
Neutral - apolitical or about someone
other than Ilhan Omar
Support - expresses support for position
or person of Ilhan Omar
Political - expression of political
difference of opinion with Ilhan Omar
Insult - personal insult directed at Ilhan
Omar not related to other labels
Immigration - Ilhan Omar has committed
fraud to remain in USA
Terrorist - Ilhan Omar is a terrorist or
supports them
Loyalty - Ilhan Omar is unAmerican,
disployal, or a traitor
Jail - Ilhan Omar should be prosecuted,
convicted, or incarcerated
Sharia - Ilhan Omar wants to replace US
law with Sharia Law
Adultery - Ilhan Omar is an adulterer or
married to her brother
31. 1 grams (muslim,islam,quran) in all 1020 Tweets
muslim (14,791), muslims (4,849), islamic (3,827),
islam (3,302,), islamist (1650), islamophobia (607),
islamists (600), quran (580), islamophobic (553),
congressmuslim (435)
32. 2 grams
a muslim (2,446), muslim brotherhood (1,631), the
muslim (1,440), islamic terrorist (594), anti muslim
(591), radical muslim (512), muslims in (483), muslim
woman (477), radical islamic (427), islam is (412)
33. 3 grams
the muslim brotherhood (624), is a muslim (488),
congressmuslim ilhan omar (376), as a muslim
(285), a muslim american (217), muslim ilhan omar
(197), muslim american trump (195), of the muslim
(192), a radical muslim (181), muslim anti
immigrant (181)
34. 4 grams
as a muslim american (198),a muslim american trump
(195),muslim american trump admirer (191),ahmed as
a muslim (183),muslim anti immigrant anti (175),she is
a muslim (171),somali congressmuslim ilhan omar
(166),omar is a muslim (151),muslim brotherhood ilhan
omar (136),muslim refugee dalia al (119)
35. as a muslim american trump (193), a muslim american
trump admirer (191), muslim american trump admirer i
(187), ahmed as a muslim american (182), muslim anti
immigrant anti black (152), qanta ahmed as a muslim
(144), icg obama isis soros muslim (117), obama isis soros
muslim brotherhood (117), isis soros muslim brotherhood
ilhan (116),omar and the progressive islamist (115)
5 grams
37. 1 grams (anti, treason, traitor, hates) in all Tweets
anti (22,403), treason (6,435), traitor
(6,324), hates (5,557), antifa (2,634),
antisemitic (1,892), treasonous (1,707),
traitors (1,702), antisemite (1,647),
antisemitism (1,308)
38. 2 grams
anti american (6455), anti semitic (3860),
anti semite (2554), a traitor (2440), for
treason (2326), she hates (2077), hates
america (2064), anti semitism (1986), an anti
(1582), is anti (1192)
39. 3 grams
is a traitor (1,065), she hates america (861),
an anti semite (661), a traitor to (570), is an
anti (564), this anti american (553), ilhan omar
hates (535), of anti semitism (514), is anti
american (478), an anti american (475)
40. 4 grams
anti semite ilhan omar (434), she is a traitor (421),
omar is a traitor (329), be hanged for treason
(311), omar is an anti (305), is a traitor to (299),
accused of anti semitism (261), ilhan omar hates
america (253), be tried for treason (246), account
suspended over treason (238)
41. 5 grams
after account suspended over treason (238), should be
hanged for treason (238), petition to demand this antisemite
(235), demand this antisemite terrorist sympathizer (235), to
demand this antisemite terrorist (235), account suspended
over treason tweet (230), this antisemite terrorist sympathizer
ilhan (228), antisemite terrorist sympathizer ilhan omar (219),
anti semite of the year (218), be hanged for treason if (216)
42. Most frequent 2 grams in (re)Tweeter profiles
#maga #kag (25416), trump supporter (18969), trump 2020 (14241), president
trump (13951), husband father (12562), pro life (11502), happily married (10383),
god family (9690), proud american (9281), god bless (9100), wife mother (8487),
lives matter (7699), love god (7609), wife mom (6833), #maga #trump2020 (6799),
maga kag (6195), jesus christ (6187), christian conservative (6103), #kag
#trump2020 (6096), family country (5749), business owner (5733), american
patriot (5055), bless america (4916), common sense (4672), #trump2020 #maga
(4478), black lives (4230), truth seeker (4138), conservative christian (4132),
father husband (3991), donald trump (3931), constitutional conservative (3908),
united states (3884), 2nd amendment (3841), mother grandmother (3811),
america great (3801), #maga #wwg1wga (3725), army veteran (3486), human
rights (3419), dog lover (3414), #wwg1wga #maga (3112), free speech (3044)
43. 1020 Annotation Labels
Neutral - apolitical or about someone
other than Ilhan Omar
Support - expresses support for position
or person of Ilhan Omar
Political - expression of political
difference of opinion with Ilhan Omar
Insult - personal insult directed at Ilhan
Omar not related to other labels
Immigration - Ilhan Omar has committed
fraud to remain in USA
Terrorist - Ilhan Omar is a terrorist or
supports them
Loyalty - Ilhan Omar is unAmerican,
disployal, or a traitor
Jail - Ilhan Omar should be prosecuted,
convicted, or incarcerated
Sharia - Ilhan Omar wants to replace US
law with Sharia Law
Adultery - Ilhan Omar is an adulterer or
married to her brother
44.
45. Lessons Learned
Impact of โlock her upโ and โsend her backโ rhetoric clearly seen in annotation.
Annotation labels must be nuanced, canโt simply label as Islamophobic or not
since hateful comments may be based on gender, race, immigration or marital
status, political beliefs in addition to or instead of religion.
A highly visible or politicized personality attracts a lot of repetitive and viral content
based on most recent accusation or conspiracy.
Profile descriptions are indicative of certain types of hateful content.
46. Current Questions
Are there correlations between public events and hateful tweet activity?
What is the impact of Tweeter location on hateful tweet activity?
Are less prominent public figures who are Muslim targeted in the same way?
Are political figures who are known to be Christian, Jewish, Hindu, and other
religions targeted to greater or lesser extents?
Can crowdsourcing be effective for more nuanced annotation problems?
47. The Way Forward
Hate Speech and Islamophobia should not be reduced to data points
Do not treat these as Just Another Classification Task
Donโt rush annotations, donโt rush to train, test, and report F-scores.
Learn the domain. Consult domain experts. Train annotators carefully.
Annotation is a great opportunity to build relationships.
Must go beyond the text to consider the characteristics of the target, the speaker,
and the context in which the speech occurs.
48. Automatically Identifying Islamophobia
in Social Media
Ted Pedersen
Department of Computer Science
University of Minnesota, Duluth
tpederse@umn.edu
@SeeTedTalk
http://www.d.umn.edu/~tpederse
53. 2 grams
sharia law (2409), under sharia (187),
wants sharia (178), for sharia (178), the
sharia (153), of sharia (137), to sharia
(129), in sharia (126), and sharia (102),
with sharia (99)
54. 3 grams
sharia law in (180), under sharia law (139),
wants sharia law (135), sharia law is (122),
sharia law and (101), in sharia law (98), for
sharia law (94), she wants sharia (90),
sharia law she (81), of sharia law (80)
55. 4 grams
she wants sharia law (68), omar calls for sharia (50),
ilhan omar suggests sharia (49), calls for sharia
flogging (48), for sharia flogging of (47), sharia
flogging of critics (46), sharia flogging for critics (44),
suggests sharia flogging for (42), omar suggests
sharia flogging (41), sharia law in the (38)
56. 5 grams
calls for sharia flogging of (47), ilhan omar calls for sharia
(47), omar calls for sharia flogging (46), for sharia flogging
of critics (45), sharia flogging of critics with (45), sharia
flogging for critics who (44), suggests sharia flogging for
critics (41), omar suggests sharia flogging for (41), ilhan
omar suggests sharia flogging (35), ilhan omar wants
sharia law (23)
58. 2 grams
a terrorist (4,282), muslim brotherhood
(1,631), the terrorist (1,151), this terrorist
(951), terrorist sympathizer (861), domestic
terrorist (752), terrorist and (669), islamic
terrorist (594), al qaeda (594), jihad rep (589)
59. 3 grams
is a terrorist (1,781), the muslim brotherhood
(624), jihad rep ilhan (561), terrorist ilhan omar
(365), a terrorist and (325), a domestic terrorist
(303), a terrorist sympathizer (268), terrorist
sympathizer ilhan (259), antisemite terrorist
sympathizer (235), this antisemite terrorist (235)
60. 4 grams
omar is a terrorist (774), jihad rep ilhan omar (558),
she is a terrorist (477), terrorist sympathizer ilhan omar
(249), this antisemite terrorist sympathizer (235),
demand this antisemite terrorist (235), antisemite
terrorist sympathizer ilhan (228), is a terrorist and
(188), she โs a terrorist (187), is a domestic terrorist
(159)
61. 5 grams
ilhan omar is a terrorist (484), demand this antisemite terrorist
sympathizer (235), to demand this antisemite terrorist (235),
this antisemite terrorist sympathizer ilhan (228), antisemite
terrorist sympathizer ilhan omar (219), terrorist sympathizer
ilhan omar u(2019)s (215), jihad rep ilhan omar praises (149),
evil jihad rep ilhan omar (148), for killing top iranian terrorist
(144), pure evil jihad rep ilhan (128)
62. Prolific (re)Tweeters in all Tweets
Founder & President @TrumpStudents (127,578), Founder and Co-Chairman of
@TrumpStudents (75,769), GOP Candidate Running Against Ilhan Omar in MN
(75,299), Republican Candidate Running Against Ilhan Omar in MN5 (60,527),
Founder & Co-Chairman @TrumpStudents (59,357), Founder & President of
@TPUSA Chairman of @TrumpStudents (55,581) , Blank (53,060), Republican
Candidate for Congress in MN5 (48,236), Republican-Endorsed Candidate
Running Against Ilhan Omar in MN5 (46,192), JudicialWatch (42,067), Father and
Former Candidate for Florida's 3rd Congressional District (40,451), Republican
Candidate Running Against Ilhan Omar in MN5 (34,889), Sean Hannity (29,355),
45th President of the United States of America (29,255), Mom, Refugee,
Intersectional Feminist, 2017 Top Angler of the Governor's Fishing Opener and
Congresswoman for #MN05 (27,888).