Combining Network and Language Indicators for Tracking Conflict Intensity

Rumshisky, Anna; Gronas, Mikhail; Potash, Peter; Dubov, Mikhail; Romanov, Alexey; Kulshreshtha, Saurabh; Gribov, Alex

doi:10.1007/978-3-319-67256-4_31

Cited by 10 publications

(16 citation statements)

References 12 publications

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“…For example, looking at the degree of involvement by using special lexical terms, e.g. slang, jargon, specialist terms, and the informal lexicons associated with social intimacy (Tausczik & Pennebaker, 2010;Rumshisky et al, 2017;Liu et al, 2015). Also, use of embeddings where concept meanings can be biased and highly impacted by the cultural background and beliefs may lead to varying interpretations (Shoemark et al, 2019;Kutuzov et al, 2018;Hamilton et al, 2016a;Dubossarsky et al, 2017;.…”

Section: Stance Utterancementioning

confidence: 99%

“…Even though embedding models consider preceding and following words of a centre word for a given sentence (context), the temporal property of the word itself and its diachronic shift from one meaning to another has not been studied in the context of stance. The identification of diachronic shift of words has however been tackled as a standalone task (Fukuhara et al, 2007;Azarbonyad et al, 2017;Tahmasebi et al, 2018;Shoemark et al, 2019;Dubossarsky et al, 2017;Stewart et al, 2017;Kutuzov et al, 2018;Hamilton et al, 2016a;Rumshisky et al, 2017). This is however yet to be explored in specific applications such as stance detection.…”

Section: General Challengesmentioning

confidence: 99%

“…In summary, understanding and detecting semantic shift (Stewart et al, 2017;Rumshisky et al, 2017;Tahmasebi et al, 2018;Shoemark et al, 2019) in the meaning of words has been of much interest in linguistics and related areas of research, including political science, history. However, the majority of this literature focuses their efforts on uncovering language evolution over time, with a dearth of computational research assessing its impact in context-based prediction models such as those using embedding models.…”

Section: General Challengesmentioning

confidence: 99%

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Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact. Opinion mining is a problem that can be operationalised by capturing and aggregating the stance of individual social media posts as supporting, opposing or being neutral towards the issue at hand. While most prior work in stance detection has investigated datasets that cover short periods of time, interest in investigating longitudinal datasets has recently increased. Evolving dynamics in linguistic and behavioural patterns observed in new data require adapting stance detection systems to deal with the changes. In this survey paper, we investigate the intersection between computational linguistics and the temporal evolution of human communication in digital media. We perform a critical review of emerging research considering dynamics, exploring different semantic and pragmatic factors that impact linguistic data in general, and stance in particular. We further discuss current directions in capturing stance dynamics in social media. We discuss the challenges encountered when dealing with stance dynamics, identify open challenges and discuss future directions in three key dimensions: utterance, context and influence.

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Section: Stance Utterancementioning

confidence: 99%

Section: General Challengesmentioning

confidence: 99%

Section: General Challengesmentioning

confidence: 99%

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Capturing stance dynamics in social media: open challenges and research directions

Alkhalifa

Zubiaga

2022

Int J Digit Humanities

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“…Identifying attitudes during the Ukrainian Crisis [85] RB (custom) DL [86] RB (POLYARNIK) DL [87] RB (SentiMental) DL [88] UNK (IQBuzz) DL [56] RB (custom) DL…”

Section: The Applications Of Sentiment Analysis For Russian Langumentioning

confidence: 99%

“…al. analysed the temporal dynamics of the political conflict as reflected in social media [87]. In contrast with Volkova's study [86], the authors did not rely on noisy location-based data to create a corpus for the analysis.…”

Section: B: Ukrainian Crisismentioning

confidence: 99%

The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

Smetanin

2020

IEEE Access

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Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we targeted the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts. INDEX TERMS Classification, machine learning, computational linguistics, sentiment analysis, applications of sentiment analysis, Russian-language texts, public opinion.

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A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

Németh

2022

J Comput Soc Sc

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As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 (n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research.

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Combining Network and Language Indicators for Tracking Conflict Intensity

Cited by 10 publications

References 12 publications

Capturing stance dynamics in social media: open challenges and research directions

Capturing stance dynamics in social media: open challenges and research directions

The Applications of Sentiment Analysis for Russian Language Texts: Current Challenges and Future Perspectives

A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

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