Adina Nerghes | Wageningen University and Research Centre (original) (raw)
Papers by Adina Nerghes
Media and Communication, 2019
The European refugee crisis received heightened attention at the beginning of September 2015, whe... more The European refugee crisis received heightened attention at the beginning of September 2015, when images of the drowned child, Aylan Kurdi, surfaced across mainstream and social media. While the flows of displaced persons, especially from the Middle East into Europe, had been ongoing until that date, this event and its coverage sparked a media firestorm. Mainstream-media content plays a major role in shaping discourse about events such as the refugee crisis, while social media's participatory affordances allow for the narratives to be perpetuated, challenged, and injected with new perspectives. In this study, the perspectives and narratives of the refugee crisis from the mainstream news and Twitter-in the days following Aylan's death-are compared and contrasted. Themes are extracted through topic modeling (LDA) and they reveal how news and Twitter converge and also diverge. We show that in the initial stages of a crisis and following the tragic death of Aylan, public discussion on Twitter was highly positive. Unlike the mainstream-media, Twitter offered an alternative and multifaceted narrative, not bound by geo-politics, raising awareness and calling for solidarity and empathy towards those affected. This study demonstrates how mainstream and social media form a new and complementary media space, where narratives are created and transformed.
By focusing on the recent events in the Middle East, that have pushed many to ee and seek refuge ... more By focusing on the recent events in the Middle East, that have pushed many to ee and seek refuge in neighboring countries or in Europe, we investigate dynamics of label use in social media, the emergent paaerns of labeling that can cause further disaaec-tion and tension, and the sentiments associated with the diierent labels. To achieve this, we examine key labels pertaining to the refugee/migrant crisis and their usage in the user comment thread of a highly viewed and informational video of the crisis on YouTube. e use of labels indicate that migration issues are being framed not only through labels characterizing the crisis but also by their describing the individuals themselves. e sentiments associated with these labels depart from what one would normally expect; in particular, negative sentiment is aaached to labels that would otherwise be deemed neutral or positive. Interestingly, both positive and negative labels exhibit increased negativity across time. Using topic modeling and sentiment analysis jointly, we discover that the latent topics of the most positive comments show more overlap than those topics of the most negative comments, which are more focused and partitioned. In terms of sentiment, we nd that labels indicating some degree of perceived agency or opportunity, such as 'migrant' or 'immigrant', are embedded in less sympathetic comments than those labels indicating a need to escape war-torn regions or persecution (e.g., asylum seeker or refugee). Our study ooers valuable insights into the direction of public sentiment and the nature of discussions surrounding this signiicant societal event, as well as the nature of online opinion sharing.
Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words ... more Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. We return to the word/document matrix using first a single text with a strong argument ("The Leiden Manifesto") and then upscale to a sample of moderate size (n = 687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the coloring of the nodes according to the results of the topic model provides maps that are difficult to interpret. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping.
Background/purpose Convenient access to vast and untapped collections of documents generated by o... more Background/purpose
Convenient access to vast and untapped collections of documents generated by organizations is a highly valuable resource for research. These documents (e.g., press releases) are a window into organizational strategies, communication patterns, and organizational behavior. However, the analysis of large document corpora requires appropriate automated methods for text mining and analysis that are able to take into account the redundant and predictable nature of formalized discourse.
Methods
We use a combination of semantic network analysis and network centrality measures to overcome these particular challenges and to explore the dynamic structural space of concepts in formalized documents pertaining to the recent financial crisis.
Data
For our analyses, we collect the press releases of the European Central Bank (ECB) and the United States’ Federal Reserve System (Fed) issued between 2006 and 2013 in order to examine their semantic networks before, during, and after the recent financial crisis. Their press releases are notably impactful in their influence on other financial institutions and society at large, especially during times of financial volatility.
Results
The structural space created from joint centrality metrics reveals salient shifts in the discursive practices of the ECB and Fed. In particular, the Fed exhibits greater attentiveness to the financial crisis especially during the crisis itself, while the ECB’s attention is delayed and increasing steadily. Furthermore, we show both the Fed’s and the ECB’s discourse transitioning into a new “hybrid” state, rather than returning to the pre-crisis status quo.
Conclusions
Examining the semantic networks of organizational text documents, we find that our analytic approach reveals important discursive shifts, which would not have been discovered under traditional text-analytic approaches. We demonstrate the utility of this approach in investigating large text corpora of organizational discourse, and we anticipate our methods to be comparably valuable in the analysis of a large spectrum of formal and informal discourse.
In this paper we will present the results of a metaphor analysis into one of the most prominent m... more In this paper we will present the results of a metaphor analysis into one of the most prominent metaphors used in media discourses surrounding the financial crisis: 'toxic'. Media sources of all kinds use the word 'toxic' in their reports about the economic crisis in many forms. 'Toxic assets',
2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014
Convenient access to vast and untapped collections of documents generated by organizations is a v... more Convenient access to vast and untapped collections of documents generated by organizations is a valuable resource for research. These documents (e.g., press releases, reports, speech transcriptions, etc.) are a window into organizational strategies, communication patterns, and organizational behavior. However, the analysis of such large document corpora does not come without challenges. Two of these challenges are 1) the need for appropriate automated methods for text mining and analysis and 2) the redundant and predictable nature of the formalized discourse contained in these collections of texts. Our article proposes an approach that performs well in overcoming these particular challenges for the analysis of documents related to the recent financial crisis. Using semantic network analysis and a combination of structural measures, we provide an approach that proves valuable for a more comprehensive analysis of large and complex semantic networks of formal discourse, such as the one of the European Central Bank (ECB). We find that identifying structural roles in the semantic network using centrality measures jointly reveals important discursive shifts in the goals of the ECB which would not be discovered under traditional text analysis approaches.
The recent financial crisis has been covered in newspapers with metaphors such as toxic assets an... more The recent financial crisis has been covered in newspapers with metaphors such as toxic assets and toxic loans. Although these groups of related metaphors (i.e., metaphor families) may strengthen the intended images on the topic under discussion, they have been only seldom studied in metaphor research. This article investigates the ways in which metaphor families fulfill a translator role for emerging terminology in financial discourses. We explore the expansion and evolution of the toxic metaphor family, revealing subtle changes of metaphor use in three newspapers over time. Our results show a transition from generic image-creating metaphors toward financial-instrument-targeted metaphors. Overall, the evidence brought by this study is a stepping-stone for further research on metaphor families.
Convenient access to vast and untapped collections of documents generated by organizations is a ... more Convenient access to vast and untapped collections
of documents generated by organizations is a valuable resource for research. These documents (e.g., press releases, reports, speech transcriptions, etc.) are a window into organizational strategies, communication patterns, and organizational behavior. However, the analysis of such large document corpora does not come without challenges. Two of these challenges are 1) the need for appropriate automated methods for text mining and analysis and 2) the redundant and predictable nature of the formalized discourse contained in these collections of texts. Our article proposes an approach that performs well in overcoming these particular challenges for the analysis of documents related to the recent financial crisis. Using semantic network analysis and a combination of structural measures, we provide an approach that proves valuable for a more comprehensive analysis of large and complex semantic networks of formal discourse, such as the one of the European Central Bank (ECB). We find that identifying structural roles in the semantic network using centrality measures jointly reveals important discursive shifts in the goals of the ECB which would not be discovered under traditional text analysis approaches.
Media and Communication, 2019
The European refugee crisis received heightened attention at the beginning of September 2015, whe... more The European refugee crisis received heightened attention at the beginning of September 2015, when images of the drowned child, Aylan Kurdi, surfaced across mainstream and social media. While the flows of displaced persons, especially from the Middle East into Europe, had been ongoing until that date, this event and its coverage sparked a media firestorm. Mainstream-media content plays a major role in shaping discourse about events such as the refugee crisis, while social media's participatory affordances allow for the narratives to be perpetuated, challenged, and injected with new perspectives. In this study, the perspectives and narratives of the refugee crisis from the mainstream news and Twitter-in the days following Aylan's death-are compared and contrasted. Themes are extracted through topic modeling (LDA) and they reveal how news and Twitter converge and also diverge. We show that in the initial stages of a crisis and following the tragic death of Aylan, public discussion on Twitter was highly positive. Unlike the mainstream-media, Twitter offered an alternative and multifaceted narrative, not bound by geo-politics, raising awareness and calling for solidarity and empathy towards those affected. This study demonstrates how mainstream and social media form a new and complementary media space, where narratives are created and transformed.
By focusing on the recent events in the Middle East, that have pushed many to ee and seek refuge ... more By focusing on the recent events in the Middle East, that have pushed many to ee and seek refuge in neighboring countries or in Europe, we investigate dynamics of label use in social media, the emergent paaerns of labeling that can cause further disaaec-tion and tension, and the sentiments associated with the diierent labels. To achieve this, we examine key labels pertaining to the refugee/migrant crisis and their usage in the user comment thread of a highly viewed and informational video of the crisis on YouTube. e use of labels indicate that migration issues are being framed not only through labels characterizing the crisis but also by their describing the individuals themselves. e sentiments associated with these labels depart from what one would normally expect; in particular, negative sentiment is aaached to labels that would otherwise be deemed neutral or positive. Interestingly, both positive and negative labels exhibit increased negativity across time. Using topic modeling and sentiment analysis jointly, we discover that the latent topics of the most positive comments show more overlap than those topics of the most negative comments, which are more focused and partitioned. In terms of sentiment, we nd that labels indicating some degree of perceived agency or opportunity, such as 'migrant' or 'immigrant', are embedded in less sympathetic comments than those labels indicating a need to escape war-torn regions or persecution (e.g., asylum seeker or refugee). Our study ooers valuable insights into the direction of public sentiment and the nature of discussions surrounding this signiicant societal event, as well as the nature of online opinion sharing.
Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words ... more Induced by "big data," "topic modeling" has become an attractive alternative to mapping co-words in terms of co-occurrences and co-absences using network techniques. We return to the word/document matrix using first a single text with a strong argument ("The Leiden Manifesto") and then upscale to a sample of moderate size (n = 687) to study the pros and cons of the two approaches in terms of the resulting possibilities for making semantic maps that can serve an argument. The results from co-word mapping (using two different routines) versus topic modeling are significantly uncorrelated. Whereas components in the co-word maps can easily be designated, the coloring of the nodes according to the results of the topic model provides maps that are difficult to interpret. In these samples, the topic models seem to reveal similarities other than semantic ones (e.g., linguistic ones). In other words, topic modeling does not replace co-word mapping.
Background/purpose Convenient access to vast and untapped collections of documents generated by o... more Background/purpose
Convenient access to vast and untapped collections of documents generated by organizations is a highly valuable resource for research. These documents (e.g., press releases) are a window into organizational strategies, communication patterns, and organizational behavior. However, the analysis of large document corpora requires appropriate automated methods for text mining and analysis that are able to take into account the redundant and predictable nature of formalized discourse.
Methods
We use a combination of semantic network analysis and network centrality measures to overcome these particular challenges and to explore the dynamic structural space of concepts in formalized documents pertaining to the recent financial crisis.
Data
For our analyses, we collect the press releases of the European Central Bank (ECB) and the United States’ Federal Reserve System (Fed) issued between 2006 and 2013 in order to examine their semantic networks before, during, and after the recent financial crisis. Their press releases are notably impactful in their influence on other financial institutions and society at large, especially during times of financial volatility.
Results
The structural space created from joint centrality metrics reveals salient shifts in the discursive practices of the ECB and Fed. In particular, the Fed exhibits greater attentiveness to the financial crisis especially during the crisis itself, while the ECB’s attention is delayed and increasing steadily. Furthermore, we show both the Fed’s and the ECB’s discourse transitioning into a new “hybrid” state, rather than returning to the pre-crisis status quo.
Conclusions
Examining the semantic networks of organizational text documents, we find that our analytic approach reveals important discursive shifts, which would not have been discovered under traditional text-analytic approaches. We demonstrate the utility of this approach in investigating large text corpora of organizational discourse, and we anticipate our methods to be comparably valuable in the analysis of a large spectrum of formal and informal discourse.
In this paper we will present the results of a metaphor analysis into one of the most prominent m... more In this paper we will present the results of a metaphor analysis into one of the most prominent metaphors used in media discourses surrounding the financial crisis: 'toxic'. Media sources of all kinds use the word 'toxic' in their reports about the economic crisis in many forms. 'Toxic assets',
2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014
Convenient access to vast and untapped collections of documents generated by organizations is a v... more Convenient access to vast and untapped collections of documents generated by organizations is a valuable resource for research. These documents (e.g., press releases, reports, speech transcriptions, etc.) are a window into organizational strategies, communication patterns, and organizational behavior. However, the analysis of such large document corpora does not come without challenges. Two of these challenges are 1) the need for appropriate automated methods for text mining and analysis and 2) the redundant and predictable nature of the formalized discourse contained in these collections of texts. Our article proposes an approach that performs well in overcoming these particular challenges for the analysis of documents related to the recent financial crisis. Using semantic network analysis and a combination of structural measures, we provide an approach that proves valuable for a more comprehensive analysis of large and complex semantic networks of formal discourse, such as the one of the European Central Bank (ECB). We find that identifying structural roles in the semantic network using centrality measures jointly reveals important discursive shifts in the goals of the ECB which would not be discovered under traditional text analysis approaches.
The recent financial crisis has been covered in newspapers with metaphors such as toxic assets an... more The recent financial crisis has been covered in newspapers with metaphors such as toxic assets and toxic loans. Although these groups of related metaphors (i.e., metaphor families) may strengthen the intended images on the topic under discussion, they have been only seldom studied in metaphor research. This article investigates the ways in which metaphor families fulfill a translator role for emerging terminology in financial discourses. We explore the expansion and evolution of the toxic metaphor family, revealing subtle changes of metaphor use in three newspapers over time. Our results show a transition from generic image-creating metaphors toward financial-instrument-targeted metaphors. Overall, the evidence brought by this study is a stepping-stone for further research on metaphor families.
Convenient access to vast and untapped collections of documents generated by organizations is a ... more Convenient access to vast and untapped collections
of documents generated by organizations is a valuable resource for research. These documents (e.g., press releases, reports, speech transcriptions, etc.) are a window into organizational strategies, communication patterns, and organizational behavior. However, the analysis of such large document corpora does not come without challenges. Two of these challenges are 1) the need for appropriate automated methods for text mining and analysis and 2) the redundant and predictable nature of the formalized discourse contained in these collections of texts. Our article proposes an approach that performs well in overcoming these particular challenges for the analysis of documents related to the recent financial crisis. Using semantic network analysis and a combination of structural measures, we provide an approach that proves valuable for a more comprehensive analysis of large and complex semantic networks of formal discourse, such as the one of the European Central Bank (ECB). We find that identifying structural roles in the semantic network using centrality measures jointly reveals important discursive shifts in the goals of the ECB which would not be discovered under traditional text analysis approaches.