== Automatisierte Datensammlung - (automated) data collection/extraction
=== data-scraping
= digital methods
==== web-scraping (minimal strukturiert)
Text(/Daten) aus Websiten extrahieren.
(Ignatow & Mihalcea, 2017, pp. 39–41)
---
===== statisch
Von statischen HTML-Websiten.
<<Rogers2013>> distinguishes between digitalized/virtual and digital methods. The former methods import standard methods from the social sciences and humanities into the emerging medium. The latter are completly new methods which emerge following the new structures and their properties. +
In this project a more inclusive conception of digital methods is assumed: the potential use of digital technology during the research.
===== dynamisch
Durch HTML5/Javascript ausgelieferte dynamische Inhalte (=> erforderliche Nutzerinteraktion).
=== parsing (dokumentierte API)
Durch Schnittstellen bereitgestellte Daten.
== data mining
Refers to the complete process of 'knowledge mining from data'.<<Han_etal2012>> Can be applied on various data types and consists of different steps and paradigms.
=== device/software-driven (tracking)
Sammlung sowie Übermittlung durch eigene Hardware/Software (bereitgestelltes Gerät, Betriebssystem, Programm, App).
=== web-crawling
Erstellung einer Sammlung von Websiten ausgehend einer Auswahl von Links und der Verfolgung darin enthaltender Verlinkungen.
(Ignatow & Mihalcea, 2017, pp. 37–39)
=== automated data collection
In principal there are multiple possible data sources in a data mining process. A basic distinction in relevance to automated data collection can be drawn between connected devices(internet, intranets) or unconnected devices(sensors, etc.). +
Furthermore the server-client-model is the established communication paradigms for connected devices. In order to obtain data either from server or client there exists three different interfaces: log files, apis and user interfaces which constitute the available procedures <<Jünger2018>>.
== Datenaufbereitung - data wrangling
Daten in maschinenlesbare Form überführen. Beispiele: PDFs, Uneinheitliche Formate in Daten
Tips zur Durchführung in R
(Wickham & Grolemund, 2017, pp. 119–260)
==== collect log-data
Collect log data which occur during providing the (web-)service or the information processing.
== data mining/ knowledge extraction
"Data mining" ist eher zu verstehen als "knowledge extraction from data". Für "data mining" existiert kein kurzes deutsches Äquivalent.
=== text mining
Auf Text bezogene Strukturanalysen.
==== parsing from api
Parse structured data from via a documented REST-API.
==== Suche
==== Frequenzanalysen
"counting things" als Methode
(Salganik, 2018, pp. 41–45)
===== Wortfrequenzanalyse - word frequency extraction
Häufigkeitsanalysen zu Wörtern Begriffen.
==== scraping
Automatically parse unstructured or semi-structured data from a normal website (⇒ web-scraping) or service.
Identify instances of specific (pre-)defined types(e.g place, name or color) in text.
===== relation extraction
Extract relationships between entities.
=== information retrieval
Retrieve relevant informations in response to the information requests.
=== indexing
'organize data in such a way that it can be easily retrieved later on'(<<Ignatow_etal2017>>,137)
=== searching/querying
'take information requests in the form of queries and return relevant documents'(<<Ignatow_etal2017>>,137). There are different models in order to estimate the similarity between records and the search queries (e.g. boolean, vector space or a probabilistic model)(ibid).
=== statistical analysis
==== frequency analysis
Descriptiv statistical analysis by using specific text abundances.
===== word frequencies/dictionary analysis
Analyse statistical significant occurence of words/word-groups. Can also be combined with meta-data (e.g. creation time of document).
===== co-occurence analysis
Analyse statistical significant co-occurence of words in different contextual units.
==== classification/machine learning
Various techniques to (semi-)automatically identify specific classes.
===== supervised classification
Use given training examples in order to classify certain entities.
===== latent semantic analysis
'The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.'(link:https://cran.r-project.org/web/packages/lsa/lsa.pdf[CRAN-R])
===== topic modelling
Probabilistic models to infer semantic clusters. See especially <<Papilloud_etal2018>>.
====== latent dirichlet allocation
'The application of LDA is based on three nested concepts: the text collection to be modelled is referred to as the corpus; one item within the corpus is a document, with words within a document called terms.(...) +
The aim of the LDA algorithm is to model a comprehensive representation of the corpus by inferring latent content variables, called topics. Regarding the level of analysis, topics are heuristically located on an intermediate level between the corpus and the documents and can be imagined as content-related categories, or clusters. (...) Since topics are hidden in the first place, no information about them is directly observable in the data. The LDA algorithm solves this problem by inferring topics from recurring patterns of word occurrence in documents.'(<<Maier_etal2018>>,94)
====== non-negative-matrix-factorization
Inclusion of non-negative constraint.
====== structural topic modelling
Inclusion of meta-data. Refer especially <<roberts2013>>.
===== Structural Topic Model
Methodendarstellung; Mögliche Anwendung bei "Open-ended Questions in Survey Experiments"
"Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations in natural language. While subjectivity classification labels text as either subjective or objective, sentiment classification adds an additional level of granularity, by further classifying subjective text as either positive, negative, or neutral." (Ignatow & Mihalcea, 2017, pp. 148–155)
'Subjectivity and sentiment analysis focuses on the automatic identification of private states, such as opinions, emotions, sentiments, evaluations, beliefs, and speculations in natural language. While subjectivity classification labels text as either subjective or objective, sentiment classification adds an additional level of granularity, by further classifying subjective text as either positive, negative, or neutral.' (<<Ignatow_etal2017>> pp. 148)
EMA und ESM unterscheiden sich nur geringfügig; EMA enthält eher medizinische Fragen und Messungen und ist auf natürliche Umgebung bezogen.
Mostly equivalent. EMA focusses on medical questions or measurements in a natural environment; ESM more on subjective Questions in the real life. Four characteristics: 1) data collection in natural environments 2) Focussing on near events/impressions/actions 3) questions triggered randomly or event-based 4) multiple questions over a certain period of time [Citation after Stone and Shiffmann 1994] (<<Salganik2018>>,109)
Enthält vier Charakteristika:
1) Datensammlung in natürlichen Umgebungen 2) Fokussierung auf zeitlich nahe Erfahrungen/ Verhaltensweisen 3) Fragen, welche Event-ausgelöst oder randomisiert gestellt werden 4) sowie mehrerer Fragen über einen bestimmten Zeitraum [Zitat nach Stone and Shiffmann 1994] (Salganik, 2018, p. 109)
==== wiki surveys
Weitere Eingrenzug von Antwortvorschlägen in Umfragen anhand Wiki-ähnlicher Umfragen.
Beispiel: http://www.allourideas.org (Salganik, 2018, pp. 111–115)
Guide open-answer questions with user feedback.
==== survey data linked to big data sources
===== Enriched asking
===== Amplified asking
=== collaborative work
==== open call projects
(e.g. annotation).
==== distributed data collection
=== digital communication
== statistical modeling
=== regression analysis
==== surveys linked to big data sources
Antwortmöglichkeiten bei Umfragen verbessern unter Rückgriff auf große Datenmengen.
(Salganik, 2018, pp. 117–130)
==== gamification in Umfragen
Strategie um Motivation von Probanden zu erhöhen.
(Salganik, 2018, pp. 115–117)
=== time-series analysis
== statistische Modellierung
=== Regressionsanalyse
Einführung mit R;
(Singh & Allen, 2017, pp. 103–152)
=== Zeitreihenanalyse
Detaillierte Einführung in aktuelle Modelle(ARMA, GARCH, VaR).
(Singh & Allen, 2017, pp. 153–182)
==== Nowcasting
Using methods to predict the future for estimation of current values. (Ex. predict influenza epidemiology combining CDC Data and Google Trends.
(Salganik, 2018, pp. 46–50)
=== agent-based modeling
=== ökonometrische Modelle
==== dynamische Modelle
"Dynamische Modelle berücksichtigen verzögerte Variablen, so dass sie in Erweiterung von statischen Mollen erlauben, die zeitliche Dynamik von wirtschaftlichen Abläufen zu beschreiben."
(Hackl, 2013, p. 286)
==== Mehrgleichungsmodelle
"Mit Mehrgleichungs-Modellen können Systeme dargestellt werden, in denen die Entwicklungen und Wechselwirkungen von mehr als einer endogenen Variablen beschrieben werden können."
Cioffi-Revilla, C. (2014). Introduction to Computational Social Science: Principles and Applications. Texts in Computer Science. London, s.l.: Springer London. Retrieved from http://dx.doi.org/10.1007/978-1-4471-5661-1
Hackl, P. (2013). Einführung in die Ökonometrie (2., aktualisierte Aufl.). Wi - Wirtschaft. München: Pearson. Retrieved from http://lib.myilibrary.com/detail.asp?id=650988
Ignatow, G., & Mihalcea, R. F. (2017). Text mining: A guidebook for the social sciences. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: Sage.
Lemke, M., & Wiedemann, Gregor (Eds.). (2016). Text Mining in den Sozialwissenschaften: Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse. Wiesbaden: Springer VS.
=== nowcasting
Using methods to predict the future for estimation of current values. (Example: predict influenza epidemiology combining CDC Data and Google Trends(<<Salganik2018>>,46–50)).
Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., . . . Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures, 12, 93–118. https://doi.org/10.1080/19312458.2018.1430754
Marres, N. (2017). Digital sociology: The reinvention of social research. Cambridge, UK, Malden, MA, USA: Polity.
[bibliography]
== References
Marres, N., & Gerlitz, C. (2016). Interface Methods: Renegotiating Relations between Digital Social Research, STS and Sociology. The Sociological Review, 64, 21–46. https://doi.org/10.1111/1467-954X.12314
- [[[Han_etal2012]]] Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Saint Louis, UNITED STATES: Elsevier Science & Technology.
Roberts, M. E., Stewart, B. M., Tingley, D., Airoldi, E. M., & others (2013). The structural topic model and applied social science. In Advances in neural information processing systems workshop on topic models: computation, application, and evaluation (pp. 1–20).
- [[[Ignatow_etal2017]]] Ignatow, G., & Mihalcea, R. F. (2017). Text mining: A guidebook for the social sciences. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: Sage.
Salganik, M. J. (2018). Bit by bit: Social research in the digital age.
- [[[Jünger2018]]] Jünger, Jakob (2018): Mapping the Field of Automated Data Collection on the Web. Data Types, Collection Approaches and their Research Logic. In: Stützer, Cathleen / Welker, Martin / Egger, Marc (Hg). Computational Social Science in the Age of Big Data. Concepts, Methodologies, Tools, and Applications. Neue Schriften zur Online-Forschung der Deutschen Gesellschaft für Online-Forschung (DGOF). Köln: Halem-Verlag, S. 104-130.
Scharkow, M. (2013). Thematic content analysis using supervisedmachine learning: An empirical evaluation using German online news. Quality & Quantity, 47, 761–773. https://doi.org/10.1007/s11135-011-9545-7
- [[[Maier_etal2018]]] Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., . . . Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures, 12(2-3), 93–118. https://doi.org/10.1080/19312458.2018.1430754
Singh, A. K., & Allen, D. E. (2017). R in Finance and Economics: WORLD SCIENTIFIC.
- [[[Papilloud_etal2018]]] Papilloud, C., & Hinneburg, A. (Eds.). (2018). Studienskripten zur Soziologie. Qualitative Textanalyse mit Topic-Modellen: Eine Einführung für Sozialwissenschaftler. Wiesbaden: Springer VS.
Sloan, L., & Quan-Haase, A. (Eds.). (2017). The SAGE handbook of social media research methods. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: SAGE reference. Retrieved from https://ebookcentral.proquest.com/lib/gbv/detail.action?docID=4771733
- [[[Roberts2013]]] Roberts, M. E., Stewart, B. M., Tingley, D., Airoldi, E. M., & others (2013). The structural topic model and applied social science. In Advances in neural information processing systems workshop on topic models: computation, application, and evaluation (pp. 1–20).
Staab, S., Koltsova, O., & Ignatov, D. I. (Eds.). (2018). Information Systems and Applications, incl. Internet/Web, and HCI: Vol. 11185. Social Informatics: 10th International Conference, SocInfo 2018, St. Petersburg, Russia, September 25-28, 2018, Proceedings, Part I. Cham: Springer International Publishing. Retrieved from https://doi.org/10.1007/978-3-030-01129-1
- [[[Rogers2013]]] Rogers, R. (2013). Digital methods. Cambridge, Massachusetts, London, England: The MIT Press.
Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, tidy, transform, visualize, and model data. Beijing, Boston, Farnham, Sebastopol, Tokyo: O'Reilly UK Ltd.
- [[[Salganik2018]]] Salganik, M. J. (2018). Bit by bit: Social research in the digital age.
- [[[Wickham_etal2017]]] Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, tidy, transform, visualize, and model data. Beijing, Boston, Farnham, Sebastopol, Tokyo: O’Reilly UK Ltd.
