Social Media and Text Mining Workshop with R

Workshop material on working with social media data and text mining methods in R

Made with woRkshoptools

Part of the conference: „Forschung zur Digitalisierung in der kulturellen Bildung“ (29-09-2022)

Contact: Veronika Batzdorfer ([email protected])

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Background

Social media are central sites of collective opinion formation and form an important basis for describing and explaining social phenomena (e.g., online radicalisation). However, when working with this type of data, decisions in all phases of the research cycle (from data collection to pre-processing steps to analytical decisions) carry risks of bias for validity and reliability aspects.

About

This workshop will include an introduction to how large amounts of text data from Twitter, which are openly available, can be made accessible and usable for research purposes. It will combine conceptual considerations and practical applications in R.

• Strategies to collect and process textual data with application programming interfaces (APIs) using common R tools.
• Potentials of bias in the research data cycle
• Basics of natural language processing (NLP), data cleaning (e.g. with 'quanteda' or 'textclean') and application of common NLP tools for automated text analysis
• Outlook on topic modelling (or word embeddings)
• Bias and ethics in NLP

Requirements

• Twitter data: Kaggle Data Dump, Depression Tweets

• Download & Installing R from: https://cran.r-project.org/

• Download & Installing RStudio from: https://www.rstudio.com/

• Dependencies

pkgs <- c("here", "lubridate", "quanteda", "quanteda.textstats", "tidyverse", 
"academictwitteR", "tibble", "kableExtra", "tidytext", 
"textclean", "academictwitteR")

install.packages(pkgs)

Contents

Slides

Time	Content
09:00 - 10:30	Concepts & challenges when analysing social web data https://github.com/nika-akin/-Social-Media-and-Text-Mining-Workshop-2022/blob/main/content/sessions/1_1_analyse_social_web_data.pdf
10:30 - 11:00	Coffee break
11:30 - 12:30	Getting Started with Twitter data: (i) Sampling, (ii) Pre-processing/ data wrangling & (iii) Basics of textual analyses (frequencies/ co-occurences/ networks) https://htmlpreview.github.io/?https://github.com/nika-akin/-Social-Media-and-Text-Mining-Workshop-2022/blob/main/content/sessions/2_1.nb.html
12:30 - 13:30	Lunch
13:30 - 15:00	Twitter Demo & Crawling Social web data https://github.com/nika-akin/-Social-Media-and-Text-Mining-Workshop-2022/blob/main/content/sessions/3_1_analyse_social_web_data.pdf
15:00 - 15:30	Coffee break
15:30 - 17:00	Outlook Advanced NLP techniques (e.g., Topic Modeling) & Social web data collection; Bias and Ethics with NLP https://github.com/nika-akin/-Social-Media-and-Text-Mining-Workshop-2022/blob/main/content/sessions/4_1_Ausblick.pdf

Data

Twitter Features

Feature ID	Type	Description
post_id	Numeric	identifier of tweet
followers	Numeric	number of followers in profile
friends	Numeric	number of friends in profile
post_created	character	date of posting tweet
post_text	character	text of original tweet
user_id	Numeric	identifier of user
label	Numeric	depression categorization: 1 = depression tweet, 2 = non-depression
favourites	Numeric	number of external favorites of the tweet
user_id	Numeric	identifier of user