Naturally leading: a content analysis of terms, themes and word associations in Natural American Spirit advertising, 2000–2020

BackgroundNatural American Spirit (NAS) is a cigarette brand distinguished by supposed ‘natural’, ‘additive-free’ characteristics, marketing of which is tied to misperceptions of reduced harm. In 2017, NAS’s manufacturer agreed (with the Food and Drug Administration) to remove ‘natural’/‘additive-free’ from US marketing. Prior research has explored NAS marketing immediately post-agreement. This study sought to identify prominent post-agreement terms and themes and analyse how they had been used in pre-agreement ads.MethodsWe conducted a content analysis of NAS ads from 2000 to 2020 (N=176), documenting prominent pre-agreement and post-agreement terms/themes and examining how they are used in NAS ads. We coded for descriptors, themes, imagery and promotions, and extended prior research by analysing how leading post-agreement terms were used in conjunction and thematically associated with ‘additive-free’ and ‘natural’ before the agreement.ResultsResults indicated ‘tobacco and water’ and ‘Real. Simple. Different.’ increased post-agreement, as did environmental imagery. ‘Organic’ was prominent pre-agreement and post-agreement. The descriptors used most often in post-agreement ads almost always appeared in conjunction with (and were thematically linked to) ‘natural’ and ‘additive-free’ in pre-agreement ads.ConclusionsIn the years since the agreement, NAS ads have heavily relied on still-allowable descriptors that may invite reduced risk misperceptions. Notably, these descriptors were consistently used alongside the banned terminology before the agreement and presented as if affiliated conceptually, possibly prompting similar connotations. Findings indicate a continuing need for research into NAS advertising effects and a potential role for additional regulatory action.

L1 mediation in Chinese English learners’ mental lexicon: Evidence from word association tests

The study aimed to explore the evidence of first language (L1) mediation in second language (L2) word associations and the L2 learner-related factors affecting the extent of L1 mediation with the following approaches. First, different from previous research, word association tests (WATs) embraced both receptive and productive word associations in the study. Second, different from word-related variables such as cognates vs. non-cognates in previous research, the study examined learner-related factors. Third, it examined whether the methodological difference (receptive vs. productive test formats) may affect the extent of L1 mediation in L2 access. A total of 108 Chinese English learners varying in proficiency, learning experiences, input and motivation participated in WATs using 24 stimulus words. The results suggested, (1) from the findings of descriptive statistics and ANOVAs, the evidence of L1 mediation was found in all three L2 proficiency groups with different extent of higher L1 mediation in lower proficiency groups. (2) From the findings of Pearson product-moment correlation coefficients, significant correlations were found with L1-promoting learning environments, L2 input, integrative motivation and the learners’ awareness of L1 activation. (3) The findings of the paired-samples t-tests confirmed a significant difference between the two test formats. Based on the findings, the study suggested the promotion of L2-rich learning environments, integrative motivation, and receptive and productive word associations in the L2 network.

Vocabulary development through reading: A comparison of approaches

<p>This thesis compares two approaches to extensive reading to determine the extent that they facilitate vocabulary development. The first approach is a traditional reading-only approach, and the second approach is a task-based approach which supplements reading with post-reading meaning-focused discussions. These two approaches are compared using a battery of tests, most notably a measure for productive knowledge of word associations. For years, scholars have believed that word associations have potential to reveal important information about a person’s language proficiency. One reason word associations are intriguing is that a large amount of a person’s lexicon can be assessed (Meara, 2009). This is possible because a large amount of data from the learner can be gathered in a short period of time. Another intriguing aspect of word association data is that it is one aspect of vocabulary knowledge that is not based on correct performance. This raises the question of an appropriate means of assigning value to the associations, a question which still hinders research to this day. Recent research has made progress in this area with a multi-level taxonomy (i.e., Fitzpatrick, 2007), creating a picture of the types of associations which exist in a learner’s lexicon. However, this taxonomy does not address the strength of the association. Wilks and Meara (2007) have attempted to tackle association strength through the use of self-report measures, whereby a test-taker reports strength of association on a four-point scale from weak to strong. This has left them with "...problems which we have not yet solved, notably a tendency for some test takers to claim that most associations are strong, while others appear to be very reluctant to identify strong associations..." (Meara, 2009, p. 80). In other words, the question of how to appropriately determine association strength is still unanswered. In the current study lexical development, in the form of word association knowledge, was measured using a multi-response word association test. Participants were assessed on their knowledge of 60 target words which occurred in five graded readers that they read over the course of the study. The learners first self-reported their knowledge of the 60 target words in terms of no knowledge, form knowledge, or meaning knowledge. The students provided up to five associations for each word that they reported at either the form or meaning levels. They did this once before reading the five graded readers, and again after finishing the graded readers. The associations provided by the students were analyzed using Latent Semantic Analysis, a method for computing semantic similarity between words (Landauer & Dumais, 1997). The associations a learner provided for each target word were assigned a similarity value representing how similar they were to the target word to which they were provided. The hypothesis was that the students who engaged in the post-reading discussion activities would show greater increases in associational knowledge of the target words than those students who did not participate in the discussions. The major finding from this thesis was that the students who struggled with a word during the post-reading discussion and were provided an opportunity to discuss the word with their group developed associational knowledge to a significantly greater degree than those students who did not encounter the words during the discussions. This emphasizes the facilitative role that meaning-focused output activities have on vocabulary development. In addition, the associational knowledge developed at the initial stages of word learning (i.e., from no knowledge to form knowledge), continued to develop from form knowledge of a word to meaning knowledge of the word, and was also developing even when words did not change in reported knowledge. This suggests a continual restructuring of the learners’ lexicon, exemplifying past research (e.g., Henriksen, 1999). Overall, the findings suggest that an extensive reading approach which includes opportunities for meaning-focused interaction has greater benefits for lexical development when compared to a traditional reading-only approach to extensive reading.</p>

Vocabulary development through reading: A comparison of approaches

<p>This thesis compares two approaches to extensive reading to determine the extent that they facilitate vocabulary development. The first approach is a traditional reading-only approach, and the second approach is a task-based approach which supplements reading with post-reading meaning-focused discussions. These two approaches are compared using a battery of tests, most notably a measure for productive knowledge of word associations. For years, scholars have believed that word associations have potential to reveal important information about a person’s language proficiency. One reason word associations are intriguing is that a large amount of a person’s lexicon can be assessed (Meara, 2009). This is possible because a large amount of data from the learner can be gathered in a short period of time. Another intriguing aspect of word association data is that it is one aspect of vocabulary knowledge that is not based on correct performance. This raises the question of an appropriate means of assigning value to the associations, a question which still hinders research to this day. Recent research has made progress in this area with a multi-level taxonomy (i.e., Fitzpatrick, 2007), creating a picture of the types of associations which exist in a learner’s lexicon. However, this taxonomy does not address the strength of the association. Wilks and Meara (2007) have attempted to tackle association strength through the use of self-report measures, whereby a test-taker reports strength of association on a four-point scale from weak to strong. This has left them with "...problems which we have not yet solved, notably a tendency for some test takers to claim that most associations are strong, while others appear to be very reluctant to identify strong associations..." (Meara, 2009, p. 80). In other words, the question of how to appropriately determine association strength is still unanswered. In the current study lexical development, in the form of word association knowledge, was measured using a multi-response word association test. Participants were assessed on their knowledge of 60 target words which occurred in five graded readers that they read over the course of the study. The learners first self-reported their knowledge of the 60 target words in terms of no knowledge, form knowledge, or meaning knowledge. The students provided up to five associations for each word that they reported at either the form or meaning levels. They did this once before reading the five graded readers, and again after finishing the graded readers. The associations provided by the students were analyzed using Latent Semantic Analysis, a method for computing semantic similarity between words (Landauer & Dumais, 1997). The associations a learner provided for each target word were assigned a similarity value representing how similar they were to the target word to which they were provided. The hypothesis was that the students who engaged in the post-reading discussion activities would show greater increases in associational knowledge of the target words than those students who did not participate in the discussions. The major finding from this thesis was that the students who struggled with a word during the post-reading discussion and were provided an opportunity to discuss the word with their group developed associational knowledge to a significantly greater degree than those students who did not encounter the words during the discussions. This emphasizes the facilitative role that meaning-focused output activities have on vocabulary development. In addition, the associational knowledge developed at the initial stages of word learning (i.e., from no knowledge to form knowledge), continued to develop from form knowledge of a word to meaning knowledge of the word, and was also developing even when words did not change in reported knowledge. This suggests a continual restructuring of the learners’ lexicon, exemplifying past research (e.g., Henriksen, 1999). Overall, the findings suggest that an extensive reading approach which includes opportunities for meaning-focused interaction has greater benefits for lexical development when compared to a traditional reading-only approach to extensive reading.</p>

A Search for Discriminative Linguistic Markers in ICT Practitioner Discourse, for the Ex Ante Identification of Disruptive Innovation

<p>Disruptive innovations have the potential to disrupt markets, and drive them in new directions. A common problem faced by business organizations is identifying such disruptive innovations. From a managerial perspective, there is real value in being able to accurately identify disruptive innovations early in the product life-cycle, as it affords the organization the opportunity to put in place business strategies that leverage this information, to gain maximal competitive advantage. This investigation was undertaken to determine if linguistic markers could be identified in ICT practitioner discourse that could be used to discriminate between traditional business intelligence (BI) - the legacy or incumbent technology, and software-as-a-service (SaaS) BI - a new technology and candidate disruptive innovation. Quantitative content analysis undertaken using the tool Veneficium WordFrequencyCounter, was used to analyze written practitioner discourse identified from within the Industry Newsgroup file of LexisNexis Academic universe. Analysis was undertaken using attribute sets derived deductively from the academic literature, and inductively from the data itself, which provided both manifest and latent meaning of component words. Individual relative word associations with both the traditional BI and SaaS BI corpora were also analyzed. Analysis of the attribute set usage data provided evidence that manifest and latent word meaning remained consistent for the time period investigated in this study (2000 to 2012), and so could support the purpose of this study, and was suggestive of the fact that SaaS BI could be a disruptive technology. The study also identified that there was a significant difference in vendor and industry attribute set usage between the SaaS BI and traditional BI corpora, consistent with the Abernathy-Utterback model. Analysis of individual word associations with the traditional BI and SaaS BI corpora identified a number of word association patterns that could discriminate between traditional BI and SaaS BI that may be transferable to other technologies. A crossover event pattern was also identified (in which the word association pattern switches between the incumbent and new technology), which may be able to provide an indication that a technology innovation is, or is about to become, disruptive. This study contributes a new approach for investigating disruptive innovation, and highlights the potential of using content analysis of practitioner discourse to identify linguistic markers for disruptive innovation. The key contribution of the study is the observation that discriminative linguistic markers can in fact be identified, and that such markers appear to have predictive capabilities. That is, they may allow organizations to identify disruptive innovations ex ante.</p>

A Search for Discriminative Linguistic Markers in ICT Practitioner Discourse, for the Ex Ante Identification of Disruptive Innovation

<p>Disruptive innovations have the potential to disrupt markets, and drive them in new directions. A common problem faced by business organizations is identifying such disruptive innovations. From a managerial perspective, there is real value in being able to accurately identify disruptive innovations early in the product life-cycle, as it affords the organization the opportunity to put in place business strategies that leverage this information, to gain maximal competitive advantage. This investigation was undertaken to determine if linguistic markers could be identified in ICT practitioner discourse that could be used to discriminate between traditional business intelligence (BI) - the legacy or incumbent technology, and software-as-a-service (SaaS) BI - a new technology and candidate disruptive innovation. Quantitative content analysis undertaken using the tool Veneficium WordFrequencyCounter, was used to analyze written practitioner discourse identified from within the Industry Newsgroup file of LexisNexis Academic universe. Analysis was undertaken using attribute sets derived deductively from the academic literature, and inductively from the data itself, which provided both manifest and latent meaning of component words. Individual relative word associations with both the traditional BI and SaaS BI corpora were also analyzed. Analysis of the attribute set usage data provided evidence that manifest and latent word meaning remained consistent for the time period investigated in this study (2000 to 2012), and so could support the purpose of this study, and was suggestive of the fact that SaaS BI could be a disruptive technology. The study also identified that there was a significant difference in vendor and industry attribute set usage between the SaaS BI and traditional BI corpora, consistent with the Abernathy-Utterback model. Analysis of individual word associations with the traditional BI and SaaS BI corpora identified a number of word association patterns that could discriminate between traditional BI and SaaS BI that may be transferable to other technologies. A crossover event pattern was also identified (in which the word association pattern switches between the incumbent and new technology), which may be able to provide an indication that a technology innovation is, or is about to become, disruptive. This study contributes a new approach for investigating disruptive innovation, and highlights the potential of using content analysis of practitioner discourse to identify linguistic markers for disruptive innovation. The key contribution of the study is the observation that discriminative linguistic markers can in fact be identified, and that such markers appear to have predictive capabilities. That is, they may allow organizations to identify disruptive innovations ex ante.</p>

Semantic network analysis of abstract and concrete word associations

In recent years, a new interest for the use of graph-theory based networks has emerged within the field of cognitive science. This has played a key role in mining the large amount of data generated by word association norms. In the present work, we applied semantic network analyses to explore norms of French word associations for concrete and abstract concepts (Lakhzoum et al., 2021). Graph analyses have shown that the network exhibits high clustering coefficient, sparse density, and small average shortest path length for both the concrete and abstract networks. These characteristics are consistent with a small-world structure. Comparisons between local node statistics and global structural topology showed that abstract and concrete concepts present a similar local connectivity but different overall patterns of structural organisation with concrete concepts presenting an organisation in densely connected communities compared to abstract concepts. These patterns confirm previously acquired knowledge about the dichotomy of abstract and concrete concepts on a larger scale. To the best of our knowledge, this is the first attempt to confirm the generalisability of these properties to the French language and with an emphasis on abstract and concrete concepts.

WinoGrande

Commonsense reasoning remains a major challenge in AI, and yet, recent progresses on benchmarks may seem to suggest otherwise. In particular, the recent neural language models have reported above 90% accuracy on the Winograd Schema Challenge (WSC), a commonsense benchmark originally designed to be unsolvable for statistical models that rely simply on word associations. This raises an important question---whether these models have truly acquired robust commonsense capabilities or they rely on spurious biases in the dataset that lead to an overestimation of the true capabilities of machine commonsense. To investigate this question, we introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC, but adjusted to improve both the scale and the hardness of the dataset. The key steps of the dataset construction consist of (1) large-scale crowdsourcing, followed by (2) systematic bias reduction using a novel AFLITE algorithm that generalizes human-detectable word associations to machine-detectable embedding associations. Our experiments demonstrate that state-of-the-art models achieve considerably lower accuracy (59.4%-79.1%) on WINOGRANDE compared to humans (94%), confirming that the high performance on the original WSC was inflated by spurious biases in the dataset. Furthermore, we report new state-of-the-art results on five related benchmarks with emphasis on their dual implications. On the one hand, they demonstrate the effectiveness of WINOGRANDE when used as a resource for transfer learning. On the other hand, the high performance on all these benchmarks suggests the extent to which spurious biases are prevalent in all such datasets, which motivates further research on algorithmic bias reduction.

On the semantic representation of risk

There is great theoretical and applied interest in understanding the psychology of risk - but what are defining features of lay people's semantic representation of this concept? We contribute a new approach to mapping the semantics of risk based on word associations that promises to provide insight into individual and group differences. Specifically, we introduce a novel mini-snowball word-association paradigm and use the tools of network and sentiment analysis to characterize the semantics of "risk" from 1,205 respondents (age range = 18-86; 50\% female). We find that association-based representations extend those extracted from past survey- and text-based approaches to the semantics of risk. Crucially, we show that the semantics of risk vary systematically across demographic groups, with older and female respondents showing more negative connotations and mentioning more often certain types of activities (e.g., recreational activities) relative to younger adults and males, respectively. Our work has implications for the measurement of risk-related constructs by suggesting that "risk" means different things to different individuals.

Semantics Graph Mining for Topic Discovery and Word Associations

Big Data creates many challenges for data mining experts, in particular in getting meanings of text data. It is beneficial for text mining to build a bridge between word embedding process and graph capacity to connect the dots and represent complex correlations between entities. In this study we examine processes of building a semantic graph model to determine word associations and discover document topics. We introduce a novel Word2Vec2Graph model that is built on top of Word2Vec word embedding model. We demonstrate how this model can be used to analyze long documents, get unexpected word associations and uncover document topics. To validate topic discovery method we transfer words to vectors and vectors to images and use CNN deep learning image classification.