Using TikTok ads for survey recruitment: a step-by-step approach

TikTok’s rapid growth and diverse user base present social science researchers with a unique opportunity to study a large and varied population, gaining valuable insights into their attitudes and behaviors. Unlike platforms such as Facebook and Instagram, TikTok’s potential in survey recruitment has been relatively underexplored. The platform’s cost-effective reach and detailed targeting parameters make it particularly appealing for reaching traditionally hard-to-reach or rare populations. Furthermore, with its video-centric format and predominantly young user base, TikTok provides a means to engage and attract respondents from younger generations to participate in online surveys. In this Methods Bites Tutorial, Zaza Zindel and Simon Lütkewitte (Bielefeld University) provide a step-by-step guide on how to use TikTok ads for survey recruitment. Continue reading

A Hands-On Introduction to Artificial Neural Networks

Neural networks are powerful machine learning algorithms that form the basis of many important technologies, including generative AI and computer vision. However, they are not as straight-forward to implement as many other machine learning techniques, like random forest or logistic regression. If you are a researcher interested in applying neural networks, this Methods Bites Tutorial by John ‘Jack’ Collins demonstrates how to get started with Artificial Neural Networks (ANN) and helps you easily prototype a neural network for your own use-case. Continue reading

BERT and Explainable AI

Natural language processing (NLP) is a fascinating field. Popular NLP techniques for understanding (written) human language include next-sentence predictions, translations, text classifications, or sentiment analysis. Such techniques already permeate our everyday lives: What would the world be without services such as Google Translate, DeepL, or the recently released ChatGPT? While common bag-of-words approaches can often be a valuable approach for NLP, Google’s release of BERT in 2018 revolutionized the possibilities in NLP. This Methods Bites Tutorial introduces the logic of large language models (LLM) with a special emphasis on BERT. It provides an applied use case from the social sciences, walks readers through explainable artificial intelligence (AI), and explains how we can leverage explainable AI to explain predictions of our models. Continue reading

Collection, Management, and Analysis of Twitter Data

As a highly relevant platform for political and social online interactions, researchers increasingly analyze Twitter data. As of 01/2021, Twitter renewed its API, which now includes access to the full history of tweets for academic usage. In this Methods Bites Tutorial, Andreas Küpfer (Technical University of Darmstadt & MZES) presents a walkthrough of the collection, management, and analysis of Twitter data. Continue reading

Using Geospatial Data in R

The use of geospatial data – data that can be mapped using geographic information systems (GIS) – has become increasingly widespread in the social sciences. Applications not only extend to the analysis of classical geographical entities (e.g., policy diffusion across spatially proximate countries) but increasingly also to analyses of micro-level data, including respondent information from georeferenced surveys or user trace data from Tweets. In this Methods Bites Tutorial, Stefan Jünger (GESIS) and Denis Cohen (MZES) show how to retrieve, manage, and visualize geospatial data in R. Continue reading

Generalized Additive Models: Allowing for some wiggle room in your models

Generalized additive models (GAMs) have become an important tool for modeling data flexibly. These models are generalized linear models where the outcome variable depends on unknown smooth functions of some predictor variables, and where the interest focuses on inference about these smooth functions. In this Methods Bites Tutorial, Sara Stoudt (Smith College) offers a hands-on recap of her workshop “Generalized Additive Models: Allowing for some wiggle room in your models” in the MZES Social Science Data Lab in March 2021. Continue reading

Extracting Emotions from Faces with Face++ (and Microsoft Azure)

Images are an increasingly used data source in the social sciences. One application is to extract features from human faces using machine learning algorithms. This blog post provides a guide on using APIs for this task, specifically how to access the services offered by Face++ and the Microsoft Face API. The post walks you through (1) how to gain API access credentials, (2) how to call the Face++ API from R, and (3) how to handle the output. It is based on the talk by Theresa Küntzler, who introduced the participants of the MZES Social Science Data Lab on May 12, 2020, to Extracting Emotions (and more) from Faces with Face++ and Microsoft Azure. Continue reading