Panning for gold: Comparative analysis of cross-platform approaches for automated detection of political content in textual data Articles uri icon

authors

  • Makhortykh, Mykola
  • de León, Ernesto
  • Urman, Aleksandra
  • GIL LOPEZ, TERESA
  • Christner, Clara
  • Sydorova, Maryna
  • Adam, Silke
  • Maier, Michaela

publication date

  • November 2024

start page

  • e0312865

issue

  • 11

volume

  • 19

International Standard Serial Number (ISSN)

  • 1932-6203

abstract

  • To understand and measure political information consumption in the high-choice media
    environment, we need new methods to trace individual interactions with online content and
    novel techniques to analyse and detect politics-related information. In this paper, we report
    the results of a comparative analysis of the performance of automated content analysis
    techniques for detecting political content in the German language across different platforms.
    Using three validation datasets, we compare the performance of three groups of detection
    techniques relying on dictionaries, classic supervised machine learning, and deep learning.
    We also examine the impact of different modes of data preprocessing on the low-cost implementations of these techniques using a large set (n = 66) of models. Our results show the limited impact of preprocessing on model performance, with the best results for less noisy data being achieved by deep learning- and classic machine learning-based models, in contrast to the more robust performance of dictionary-based models on noisy data.

subjects

  • Information Science