Analysing and Extracting Data

Code:

DCCI03

Acronym:

AED

Keywords
Classification	Keyword
OFICIAL	Social Science

Instance: 2024/2025 - 2S

Active?	Yes
Responsible unit:	Department of Communication and Information Sciences
Course/CS Responsible:	Communication and Information Sciences

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
DCCI	0	study plan	1	-	9	61	243

Teaching language

Portuguese

Objectives

• Acquire a broad understanding of the strengths and limitations of the various methodologies for analyzing and extracting data in Social Sciences.


• Understand the history and trajectory of the field of social computing


• Be able to solve problems – organizing and planning work to collect and analyze large amounts of digital data.

Learning outcomes and competences

(1) to understand the fundamentals of digital data analysis
(2) to develop a critical attitude towards issues that may be the object of analysis
(3) to understand the potential and limitations of the various extraction methodologies and data analysis.

Working method

Presencial

Program

1. Introduction to methods of extracting and analyzing digital data


2. Texts as data: the massive extraction of text from digital networks and its analysis


3. Analysis of online interaction norms and styles: extracting data from social networks and analyzing the social dynamics of communities


4. The path from data to conclusions: issues of bias, validity and generalization


5. Algorithms and society

Mandatory literature

Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. ; Computational social science and sociology, Annual Review of Sociology, 46, 61–81. https://doi.org/10.1146/annurev-soc-121919-054621, 2020
Theocharis, Y., & Jungherr, A. ; Computational social science and the study of political communication., Political Communication, 36(1–2), 1–22. https://doi.org/10.1080/10584609.2020.1833121, 2020

Teaching methods and learning activities

Practical theoretical classes in laboratory format. Initially, a set of problems that imply extraction and analysis of digital data are introduced. Its resolution is carried out in a hands-on format of experimentation and discussion in class.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho laboratorial	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Trabalho laboratorial	61,00
Total:	61,00

Eligibility for exams

75% presence in class

Calculation formula of final grade

Evaluation of final project