Paragraph-based representation of texts: A complex networks approach

Arruda, Henrique Ferraz de; Marinho, Vanessa Queiroz; Costa, Luciano da Fontoura; Amancio, Diego R.

doi:10.1016/j.ipm.2018.12.008

Cited by 35 publications

(33 citation statements)

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“…Empirical and theoretical research in the cognitive sciences identified these mental representations of knowledge as components of a way more complex system called mental lexicon, a repository of knowledge apt at information acquisition, processing, and use [21]. The recent adoption of network science tools has shown how the large-scale, associative structure of word knowledge in the mental lexicon is highly informative of a wide variety of cognitive processes such as lexical processing [19,22,23,24], learning and cognitive development [25,26,27], text structuring and writing styles [28,29,30,31], creativity [32,33], and expertise levels in specific domains [34,5]. Analogously, forma mentis networks act as approximated reconstructions on the mental constructs built by individuals in their associative mental lexicon, representing their perceptions of the outer world [16,17].…”

Section: Introductionmentioning

confidence: 99%

Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School

Stella¹

2020

Education Sciences

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This study investigates how students and researchers shape their knowledge and perception of educational topics. The mindset or forma mentis of 159 Italian high school students and of 59 international researchers in science, technology, engineering and maths (STEM) are reconstructed through forma mentis networks, i.e., cognitive networks of concepts connected by free associations and enriched with sentiment labels. The layout of conceptual associations between positively/negatively/neutrally perceived concepts is informative on how people build their own mental constructs or beliefs about specific topics. Researchers displayed mixed positive/neutral mental representations of "teacher", "student" and, "scientist". Students' conceptual associations of "scientist" were highly positive and largely non-stereotypical, although links about the "mad scientist" stereotype persisted. Students perceived "teacher" as a complex figure, associated with positive aspects like mentoring/knowledge transmission but also to negative sides revolving around testing and grading. "School" elicited stronger differences between the two groups. In the students' mindset, "school" was surrounded by a negative emotional aura or set of associations, indicating an anxious perception of the school setting, mixing scholastic concepts, anxiety-eliciting words, STEM disciplines like maths and physics, and exam-related notions. Researchers' positive stance of "school" included concepts of fun, friendship, and personal growth instead. Along the perspective of Education Research, the above results are discussed as quantitative evidence for test-and STEM anxiety co-occurring in the way Italian students perceive education places and their actors. Detecting these patterns in student populations through forma mentis networks offers new, simple to gather yet detailed knowledge for future data-informed intervention policies and action research.

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Section: Introductionmentioning

confidence: 99%

Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School

Stella¹

2020

Education Sciences

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“…Within the literature of network-powered analyses of large volumes of text (de Arruda et al, 2019;Koponen and Nousiainen, 2019), previous works successfully used word co-occurrences in text (e.g. "like" and "stem" occurring one after the other in text, cf.…”

Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

“…(Cancho and Solé, 2001)) in order to characterise language content through average statistical markers (Amancio et al, 2012;Amancio, 2015) or timeevolving dynamics (Akimushkin et al, 2017). These networks were considerably powerful at a global level and very successful in tasks like author identification (Amancio, 2015;Akimushkin et al, 2017;de Arruda et al, 2019). However, the validity of co-occurrence networks as representations of the mental lexicon at the microscopic level of individual conceptual associations has been recently reconsidered (Ninio, 2014;Rizvi, 2018).…”

Section: Literature Review On Relevant Past Approachesmentioning

confidence: 99%

“…From a methodological perspective, TFMNs rely on the recent theory of cognitive networks (Siew et al, 2019) and include syntactic and semantic conceptual associations which are informative of the structure of knowledge perceived by text authors (Stella et al, 2018a;Stella, 2019). Differently from other successful models of knowledge representation such as concept maps (Koponen and Nousiainen, 2019;Dóczi, 2019) and knowledge graphs (Amancio, 2015;Akimushkin et al, 2017;de Arruda et al, 2019), forma mentis networks contain also emotional information, outlining sentiment and emotional patterns in the way individuals assembled their stance in a text. This contextual information is essential for the interpretability of a detected stance (Nasar et al, 2019).…”

Section: Computer Sciencementioning

confidence: 99%

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Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

2020

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Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e. the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets' structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals' opinions at the beginning of posts. "Gender" and "gap" elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science "sacrificing personal skills for success". The semantic frame of "man" highlighted awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that tweets got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. This analysis thus identified the Twitter discourse mostly starting conversations as promoting a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users' replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.

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“…The key difference between one and othersthem is the use of the forms of a lexeme to build words and sentences. In this context, many methods developed in the study of time-series analysis and network science are potentially suitable to evaluate this kind of structural differences [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Recurrence Networks in Natural Languages

Baeza-Blancas

Obregón-Quintana

Hernández-Gómez³

et al. 2019

Entropy

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We present a study of natural language using the recurrence network method. In our approach, the repetition of patterns of characters is evaluated without considering the word structure in written texts from different natural languages. Our dataset comprises 85 ebookseBooks written in 17 different European languages. The similarity between patterns of length m is determined by the Hamming distance and a value r is considered to define a matching between two patterns, i.e., a repetition is defined if the Hamming distance is equal or less than the given threshold value r. In this way, we calculate the adjacency matrix, where a connection between two nodes exists when a matching occurs. Next, the recurrence network is constructed for the texts and some representative network metrics are calculated. Our results show that average values of network density, clustering, and assortativity are larger than their corresponding shuffled versions, while for metrics like such as closeness, both original and random sequences exhibit similar values. Moreover, our calculations show similar average values for density among languages which that belong to the same linguistic family. In addition, the application of a linear discriminant analysis leads to well-separated clusters of family languages based on based on the network-density properties. Finally, we discuss our results in the context of the general characteristics of written texts.

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Paragraph-based representation of texts: A complex networks approach

Cited by 35 publications

References 54 publications

Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School

Forma Mentis Networks Reconstruct How Italian High Schoolers and International STEM Experts Perceive Teachers, Students, Scientists, and School

Peer Review #1 of "Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media (v0.2)"

Recurrence Networks in Natural Languages

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