scholarly journals Text Analysis Methods for Misinformation–Related Research on Finnish Language Twitter

2021 ◽  
Vol 13 (6) ◽  
pp. 157
Author(s):  
Jari Jussila ◽  
Anu Helena Suominen ◽  
Atte Partanen ◽  
Tapani Honkanen
Keyword(s):  
Data Analysis ◽  
Automated Analysis ◽  
Word Formation ◽  
Fake News ◽  
Word Count ◽  
Twitter Data ◽  
Analysis Methods ◽  
Twitter Data Analysis ◽  
Language Areas ◽  
Data Analysis Methods

The dissemination of disinformation and fabricated content on social media is growing. Yet little is known of what the functional Twitter data analysis methods are for languages (such as Finnish) that include word formation with endings and word stems together with derivation and compounding. Furthermore, there is a need to understand which themes linked with misinformation—and the concepts related to it—manifest in different countries and language areas in Twitter discourse. To address this issue, this study explores misinformation and its related concepts: disinformation, fake news, and propaganda in Finnish language tweets. We utilized (1) word cloud clustering, (2) topic modeling, and (3) word count analysis and clustering to detect and analyze misinformation-related concepts and themes connected to those concepts in Finnish language Twitter discussions. Our results are two-fold: (1) those concerning the functional data analysis methods and (2) those about the themes connected in discourse to the misinformation-related concepts. We noticed that each utilized method individually has critical limitations, especially all the automated analysis methods processing for the Finnish language, yet when combined they bring value to the analysis. Moreover, we discovered that politics, both internal and external, are prominent in the Twitter discussions in connection with misinformation and its related concepts of disinformation, fake news, and propaganda.

scholarly journals Literature review on Real-time Location-Based Sentiment Analysis on Twitter

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dilmini Rathnayaka ◽  
Pubudu K.P.N Jayasena ◽  
Iraj Ratnayake
Keyword(s):  
Social Media ◽  
Data Analysis ◽  
Sentiment Analysis ◽  
Real Time ◽  
Analysis Data ◽  
Time Data ◽  
Twitter Data ◽  
Analysis Methods ◽  
Real Time Data ◽  
Twitter Data Analysis

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

scholarly journals Objective assessment of SERS thin films: comparison of silver on copper via galvanic displacement with commercially available fabricated substrates

2017 ◽  
Vol 9 (33) ◽  
pp. 4783-4789 ◽  
Author(s):  
Samuel Mabbott ◽  
Yun Xu ◽  
Royston Goodacre
Keyword(s):  
Thin Films ◽  
Data Analysis ◽  
Objective Assessment ◽  
Galvanic Displacement ◽  
Sers Signal ◽  
Analysis Methods ◽  
Data Analysis Methods

Reproducibility of SERS signal acquired from thin films developed in-house and commercially has been assessed using seven data analysis methods.

Omaha System Public Health Data: Analysis Methods

2010 ◽  
Vol 58 (2) ◽  
pp. e22-e23
Author(s):  
Karen A. Monsen ◽  
Karen S. Martin ◽  
Bonnie L Westra
Keyword(s):  
Public Health ◽  
Data Analysis ◽  
Health Data ◽  
Public Health Data ◽  
Analysis Methods ◽  
Omaha System ◽  
Data Analysis Methods

Peak detection in sediment - charcoal records: impacts of alternative data analysis methods on fire-history interpretations

2010 ◽  
Vol 19 (8) ◽  
pp. 996 ◽  
Author(s):  
Philip E. Higuera ◽  
Daniel G. Gavin ◽  
Patrick J. Bartlein ◽  
Douglas J. Hallett
Keyword(s):  
Data Analysis ◽  
Fire History ◽  
Threshold Value ◽  
Decomposition Methods ◽  
Peak Detection ◽  
Detection Methods ◽  
Statistical Framework ◽  
Analysis Methods ◽  
Decomposition Models ◽  
Data Analysis Methods

Over the past several decades, high-resolution sediment–charcoal records have been increasingly used to reconstruct local fire history. Data analysis methods usually involve a decomposition that detrends a charcoal series and then applies a threshold value to isolate individual peaks, which are interpreted as fire episodes. Despite the proliferation of these studies, methods have evolved largely in the absence of a thorough statistical framework. We describe eight alternative decomposition models (four detrending methods used with two threshold-determination methods) and evaluate their sensitivity to a set of known parameters integrated into simulated charcoal records. Results indicate that the combination of a globally defined threshold with specific detrending methods can produce strongly biased results, depending on whether or not variance in a charcoal record is stationary through time. These biases are largely eliminated by using a locally defined threshold, which adapts to changes in variability throughout a charcoal record. Applying the alternative decomposition methods on three previously published charcoal records largely supports our conclusions from simulated records. We also present a minimum-count test for empirical records, which reduces the likelihood of false positives when charcoal counts are low. We conclude by discussing how to evaluate when peak detection methods are warranted with a given sediment–charcoal record.

scholarly journals Weighing the Giants – I. Weak-lensing masses for 51 massive galaxy clusters: project overview, data analysis methods and cluster images

2014 ◽  
Vol 439 (1) ◽  
pp. 2-27 ◽  
Author(s):  
Anja von der Linden ◽  
Mark T. Allen ◽  
Douglas E. Applegate ◽  
Patrick L. Kelly ◽  
Steven W. Allen ◽  
...  
Keyword(s):  
Data Analysis ◽  
Galaxy Clusters ◽  
Weak Lensing ◽  
Analysis Methods ◽  
Massive Galaxy ◽  
Data Analysis Methods
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