I have taught several courses at the Communication, Culture and Technology Program at Georgetown University, from 2012 to 2017, exploring how new media can be analyzed to gain insight into social and cultural issues. My courses taught computational methods to students with diverse academic backgrounds that can be applied for content analysis, social network analysis, and discourse analysis in social media. Courses focus on technology, internet linguistics, language and politics, and the role of new technological of communication in the propagation of social and political debate.


Georgetown University, Washington DC

  • Sentiment Analysis and Opinion Mining in Social Media 
    The course explores the core concepts of sentiment analysis and opinion mining in social media, to understand the state-of-the-art in the field, and to investigate the limitations of these approaches, by studying recent publications and research applying sentiment analysis in the domains of political science, clinical studies, marketing and deception detection.
    Students gain hands-on experience in using some of the tools and technology available for automatically downloading Twitter data and performing sentiment analysis on the text. Automatic tools allow the researcher to go beyond manual analysis of a limited data set to detect emerging trends, automatically identify opinions in public debate or product reviews, and investigate changes in time. We will also discuss the combination of qualitative and quantitative measures applied to identifying opinions and sarcasm in social media. As part of the final project, the students will have an opportunity to examine sentiment analysis on a topic of their choice.

  • Narrative Networks: Analysis of Framing and Narratives in Online Media 
    Narratives are used to convey a certain perspective or to craft an identity by tapping into preexisting beliefs and opinions. Narratives are also the central mechanism through which ideologies are expressed and absorbed in the population. The study of narratives and framing theory is therefore a fundamental component of media studies, strategic communications, organization branding and policy setting. The course focuses on the core concepts of narrative theory in online news and social media, and introduces the students to technology that can be applied for the automatic identification of framing and narrative. We will apply content and corpus analysis to detect important frames and rhetoric, and social network analysis to identify distinct factions and perspectives. Computer skills are not required for this course; technical concepts will be presented through readings and hands-on applications in the classroom.

  • Social Media Analytics: Culture and Ideology in the Middle East
    The goal of this course was to discover the socio-cultural issues in the Middle East through the application of Social Media Analysis technologies. The focus of this seminar is on Social Media Analysis technology with emphasis on Twitter and Blogs and its application to several Middle Eastern nations. The goal is to discover the main issues, cultural and religious forces, minority groups, women s roles, and youth interests by automatically investigating the trends and discussions found in new media sources. 

  • Linguistics, Computation, and Social Media
    Yerevan Academy for Linguistics and Philosophy (YALP 2017)
    American University of Armenia
    Social Media have played an instrumental role in creating a new forum for discussion for people, allowing researchers to access information that didn't readily exist before and to ask new questions that may not have been available in the past. This course provides an introduction to Computational Linguistics and Natural Language Processing by exploring the technology used in social media analysis. The course will present an overview of the recent technological approaches that allow researchers to automatically extract meaning from social data. We will explore technology to identify social groups in online networks, detect emerging topics and trends, determine the author's characteristics such as age and gender, and perform sentiment analysis. Students will have an opportunity to apply some of these analyses to the content of Twitter through hands-on applications in the classroom. Computer skills are not required for this course.


  • Introduction to Computational Linguistics
    Yerevan Academy for Linguistics and Philosophy (YALP 2018)
    American University of Armenia

    The course offers an introduction to Computational Linguistics, which incorporates research and techniques for processing language using computers at all levels of linguistic structure - including morphology, morpho-syntax, syntax and lexical semantics. The class will provide an overview of various topics and tasks in computational linguistics, from a linguist's perspective. These include applications such as machine translation, social media analytics, and sentiment analysis. We will discuss knowledge-based approaches in building morphological analyzers and Part-of-Speech taggers, as well as statistical approaches (e.g., n-gram analysis, syntactic parsing, classification with machine learning). The goal of the class is to provide a sense of the state of the art in the field, the main approaches used, and an understanding of how to conceptualize and solve problems in computational linguistics. No computer or programming knowledge is required.

  • Heritage Language Grammar and Language Analysis (Persian and Armenian)
    University of California, San Diego (2004-2005)

    The course teach the heritage language grammar to heritage students of Persian and Armenian through a discovery process. 


Yerevan, Armenia