Connections between the lines

Chang, Jonathan; Boyd-Graber, Jordan; Blei, David M.

doi:10.1145/1557019.1557044

Cited by 193 publications

(151 citation statements)

References 25 publications

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“…We note that an LDA model uses randomness in its training and inference, therefore training a new model with the same parameters will always yield slightly different topic distributions and there are different ways and limitations to analyze those [ 35 ].…”

Section: To What Extent Is the War Figurative Frame Used To Talk Aboumentioning

confidence: 99%

Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

2020

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Doctors and nurses in these weeks and months are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. The discourse around the current epidemic makes use of war-related metaphors too, not only in public discourse and in the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a large corpus tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSU-NAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY frame covers a wider portion of the corpus, among the figurative frames WAR, a highly conventional one, is the frame used most frequently. Yet, this frame does not seem to be apt to elaborate the discourse around some aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options-or a metaphor menu-may facilitate the communication of various aspects involved in the Covid-19related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and beliefs during the current pandemic.

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Section: To What Extent Is the War Figurative Frame Used To Talk Aboumentioning

confidence: 99%

Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

2020

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“…We assessed success in our case study in three ways: (1) by the effectiveness of the process in leading non-experts to drill down to highly-relevant content in a very large collection of books; (2) by the ability of this process to spotlight a somewhat forgotten woman scientist who is important to the history of psychology; (3) by the capacity of the process to lead domain experts to a surprising discovery about the breadth of species discussed in these historical materials, thus enriching the historical context for current discussions of intelligence in microscopic organisms [ 34 , 35 ]. Our assessments are qualitative rather than quantitative in nature, but they are appropriate given current limitations in quantitative assessments of the quality of topic models [ 36 – 38 ].…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies indicate a general consensus that human judgments about what makes a “good” topic are generally convergent. However, human judgment does not typically correlate well with quantitative measures of model fit [ 36 ], suggesting that people are interpreting the topics using as-yet poorly understood semantic criteria. Furthermore, variation among people in their interpretation of topic quality may be dependent upon expertise.…”

Section: Introductionmentioning

confidence: 99%

Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library

et al. 2017

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We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of “drill-down” topic modeling—simultaneously reducing both the size of the corpus and the unit of analysis—we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of “close reading” needed to generate original interpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted.

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“…With regard to the second class of models for networks extracted from text, Named Entity Recognition methods are typically used to identify the nodes and co-occurrence (or other language analysis approaches) to create edges among them [48,24]. In this case, the output network connects different portions of a text document, or concepts extracted from the text.…”

Section: Text and Topologymentioning

confidence: 99%

“…For example, HINs have been used in the past to model co-occurrence relations between entities (e.g., famous characters, sports, companies) in Wikipedia articles [26]. In [24] vertices represent either famous characters from the text or bags of words, while the edges connect words that best explain the contexts where two or more famous characters appear together in the text. Document-phrase graphs as defined in [23] are also HIN-based models, and more in detail probabilistic bipartite networks B = (V, U, E, W ) where the vertices in one partition V represent documents from a large document collection, the vertices in U represent salient phrases which are semantically relevant to one or more documents in V , and edges E indicate the relevance of each sentence for each document.…”

Section: Text and Topologymentioning

confidence: 99%

Foundations of Temporal Text Networks

Vega

Magnani

2018

Appl Netw Sci

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Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.

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Connections between the lines

Cited by 193 publications

References 25 publications

Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library

Foundations of Temporal Text Networks

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