Relating social and symbolic relations in quantitative text analysis. A study of parliamentary discourse in the Weimar Republic

Fuhse, Jan; Stuhler, Oscar; Riebling, Jan Rasmus; Martin, John Levi

doi:10.1016/j.poetic.2019.04.004

Cited by 38 publications

(27 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Closely related to this are approaches that construct word co-occurrence networks and then cluster them using community detection algorithms (Fuhse et al 2020;Rule et al 2015;Hoffman et al 2018).…”

Section: Measuring Political Frames In Texts Previous Approachesmentioning

confidence: 99%

Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns (1952-2020) with Deep Neural Language Models

Bonikowski¹,

Luo²,

Stuhler³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Radical-right parties and candidates combine three discursive elements in their electoral appeals: anti-elite populism, exclusionary and declinist nationalism, and illiberal authoritarianism. Recent studies have explored whether these frames have diffused from radical-right to centrist parties in the latter's effort to compete for the former's voters. This paper investigates the obverse process: the radical right's (specifically, Donald Trump's) reliance on discursive elements that had long been present in mainstream institutional politics. To do so, we identify instances of populism, nationalism (i.e., exclusionary and inclusive definitions of national symbolic boundaries and displays of low and high national pride), and authoritarianism in the speeches of Democratic and Republican presidential nominees between 1952 and 2016. These frames are subtle, infrequent, and polysemic, which makes their quantitative measurement difficult. We overcome this by leveraging the affordances of cutting-edge neural language models; in particular, we combine a variant of bidirectional encoder representations from transformers (RoBERTa) with active learning. As we demonstrate, this approach is considerably more powerful than other methods commonly used by social scientists to measure discursive frames. Our results suggest that what set Donald Trump's campaign apart from those of mainstream presidential candidates was not its invention of a new form of politics, but its combination of negative evaluations of elites, low national pride, and authoritarianism---all of which had long been present among both parties---with an explicit evocation of exclusionary nationalism, which had previously been used only in coded form. Radical-right discourse therefore appears to be less a break with the past and more an amplification and creative rearrangement of existing political-cultural tropes.

show abstract

Section: Measuring Political Frames In Texts Previous Approachesmentioning

confidence: 99%

Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns (1952-2020) with Deep Neural Language Models

Bonikowski¹,

Luo²,

Stuhler³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Notice that our ultimate goal is to identify mental and conceptual associations which are on average most typical for suicide notes. Hence, we do not take a sociological perspective focused on evolution of collective narrative strategies in connection with other social processes 30 , 31 . Instead, we aim at identifying cognitive patterns which are common across different individuals who committed suicide.…”

Section: Introductionmentioning

confidence: 99%

Revealing semantic and emotional structure of suicide notes with cognitive network science

Teixeira

Talaga

Swanson

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is “love”, which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of “love” combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.

show abstract

“…As BD is not only quantitative but also qualitative in nature, this in theory allows for the study of subjective meanings as in qualitative research but on a much greater scale (Fuhse et al 2020). However, the extraction of meaning from large bodies of text via algorithms still seems to be a challenging prospect (cf.…”

Section: Critical Perspectives On Bdmentioning

confidence: 99%

Big Data: Some Ethical Concerns for the Social Sciences

Weinhardt

2021

Social Sciences

View full text Add to dashboard Cite

While big data (BD) has been around for a while now, the social sciences have been comparatively cautious in its adoption for research purposes. This article briefly discusses the scope and variety of BD, and its research potential and ethical implications for the social sciences and sociology, which derive from these characteristics. For example, BD allows for the analysis of actual (online) behavior and the analysis of networks on a grand scale. The sheer volume and variety of data allow for the detection of rare patterns and behaviors that would otherwise go unnoticed. However, there are also a range of ethical issues of BD that need consideration. These entail, amongst others, the imperative for documentation and dissemination of methods, data, and results, the problems of anonymization and re-identification, and the questions surrounding the ability of stakeholders in big data research and institutionalized bodies to handle ethical issues. There are also grave risks involved in the (mis)use of BD, as it holds great value for companies, criminals, and state actors alike. The article concludes that BD holds great potential for the social sciences, but that there are still a range of practical and ethical issues that need addressing.

show abstract

Relating social and symbolic relations in quantitative text analysis. A study of parliamentary discourse in the Weimar Republic

Cited by 38 publications

References 42 publications

Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns (1952-2020) with Deep Neural Language Models

Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns (1952-2020) with Deep Neural Language Models

Revealing semantic and emotional structure of suicide notes with cognitive network science

Big Data: Some Ethical Concerns for the Social Sciences

Contact Info

Product

Resources

About