Non-lexical Features Encode Political Affiliation on Twitter

Tatman, Rachael; Stewart, Leo G; Paullada, Amandalynne; Spiro, Emma S.

doi:10.18653/v1/w17-2909

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Similarly, Kosovo officials are also referred to within quotation marks, for example the "Minister of Foreign Affairs of Kosovo" ("Enver Hodžaj", 2017). This is in agreement with the study of Rachael Tatman et al, in which they have come to the conclusion that non-lexical textual patterns allow the possibility of divulging the author's political affiliation, punctuation being one of the revealing instances (Tatman, Stewart, Paullada, & Spiro, 2017), in this case, the excessive use of parentheses, coupled with a repetitive lexical choice. This lexical selection commonly surfaces in the form of the "fake" or "so-called" state of Kosovo, confirming R. Sherlock Campbell and James W. Pennebaker's research on pronouns as most important signifiers within qualitative content analysis (Campbell & Pennebaker, 2003).…”

Section: Case Studiessupporting

confidence: 91%

Confronting recent history: Serbia’s ‘fateful eight’ and a decade of Kosovo’s independence in the Serbian Media (2008–2018)

Jovanović

2018

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Introduction T aking a look into Serbia's recent history reveals Serbia's own recent "fateful eight"the declaration of Kosovo's independence in 2008, when the last part broke off from the carcass of former Yugoslavia. Though other significant eights have figured in Serbian (and Yugoslav) history -such as the 1918 creation of the Kingdom of Serbs, Croats and Slovenes, or the 1968 student protests -Kosovo's declaration of independence, as a recent "addition" to the list, has figured profusely and significantly in Serbia's recent history, especially within the media. Serbian media -traditional and electronic -have figured as a key centre of disseminating Kosovo-based Serbian nationalism since 2008 and the declaration of independence. While there is a significant body of research on the Serbia-Kosovo issue, the role of the media in publicising nationalist discourse centred around Kosovo still has not seen its day in scholarship. In this article, I shall tackle Kosovo-based Serbian nationalism in recent

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Section: Case Studiessupporting

confidence: 91%

Confronting recent history: Serbia’s ‘fateful eight’ and a decade of Kosovo’s independence in the Serbian Media (2008–2018)

Jovanović

2018

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“…Smaller proportion of replies with more punctuation and capitalisation were noted in the case of right-winged users. Although there were robust differences between left and right leaning groups with respect to their discourse and sub-lexical features, their predictive accuracy was low (Tatman, Stewart, Paullada, & Spiro, 2017). In a study on the linguistic features present in populist discourse on tweets by four European political leaders-Luigi Di Maio, Matteo Salvini, Marine Le Pen and Nigel Farage and its relationship with their popularity, Carrella (2018) identified emotionalization, simplistic rhetoric and intensified evaluations as the discursive elements of populistic expressions.…”

Section: Linguistic Analysismentioning

confidence: 99%

Linguistic Implications of Political Tweets

Alshahrani¹

2020

IJEL

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Among the social media, Twitter is widely used by political leaders and the public to express opinions about various political issues. These tweets may influence the course of major political events like elections, Brexit and popularity of certain politicians. This common observation led to the research question of this paper: What are the political implications of Twitter postings? To answer this research question, an exploratory qualitative review of tweets was undertaken. Google Scholar was searched using the topic itself as the search term twice with two different time frames till 2019. The search yielded 41 papers. The papers were listed with brief description. A table categorising the methods used in the papers was useful to derive some conclusions. Generally, Twitter can be used for both positive and negative purposes and it can impact either or both the leader and the people who read them. Certain factors are involved in determining the nature of post and its outcomes. Many theories and methods had been used in the papers. Manual, machine learning and automatic analytical tools have been tested and used widely. None of the methods is perfectly suitable for all types of Twitter analysis. General content textual descriptions, characteristics of the texts, symbols, hidden meanings and presentation methods have been used in the tweets examined by the authors. The potential of negative tweets and hate speeches is quite clear. Absence of internal standard definition of these types of posts stands in the way of effective prevention. Some recommendations have been listed based on the findings of this review. A few limitations of this review have also been listed.

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“…One common task is identifying political affiliation. Both linguistic and non-linguistic features have proven effective in classifying political leanings of users in Twitter data (Tatman et al, 2017). Preoiuc-Pietro et al (2017) provide a method for identifying whether users are liberal or conservative, and point to a variety of user level classifications that can be predictive of political ideology.…”

Section: Task 2: Evacuation Classificationmentioning

confidence: 99%

Improving Classification of Twitter Behavior During Hurricane Events

Stowe¹,

Anderson²,

Palmer³

et al. 2018

Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media

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A large amount of social media data is generated during natural disasters, and identifying the relevant portions of this data is critical for researchers attempting to understand human behavior, the effects of information sources, and preparatory actions undertaken during these events. In order to classify human behavior during hazard events, we employ machine learning for two tasks: identifying hurricane related tweets and classifying user evacuation behavior during hurricanes. We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.

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Non-lexical Features Encode Political Affiliation on Twitter

Cited by 7 publications

References 14 publications

Confronting recent history: Serbia’s ‘fateful eight’ and a decade of Kosovo’s independence in the Serbian Media (2008–2018)

Confronting recent history: Serbia’s ‘fateful eight’ and a decade of Kosovo’s independence in the Serbian Media (2008–2018)

Linguistic Implications of Political Tweets

Improving Classification of Twitter Behavior During Hurricane Events

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