Historical Portrayal of G reek Tourism through Topic Modeling on International Newspapers (2024)

Abstract: In this paper, we bridge computational linguistics with historical methods to explore the potential of topic modeling in historical newspapers. Our case study focuses on British and American newspapers published in the second half of the 20th century that debate issues of Greek tourism, but our method can be transposed to any diachronic data. We demonstrate that Non-negative Matrix Factorization (NFM) can generate interpretable topics within the historical period under examination providing a tangible example of how computational text analysis can assist historical research. The contribution of our work is two-fold; first, the extracted topics are evaluated both by a computational linguist and by a historian highlighting the crucial role of domain experts when interpreting topic modeling outputs. Second, the extracted topics are contextualized within the historical and political environment in which they appear, providing interesting insights about the historical representations of Greek tourism over the years, and about the development and the hallmarks of American and British tourism in Greece across different historical periods (from 1945 to 1989). The comparative analysis between the American and the British press reveals interesting insights including similar responses to specific events as well as notable differences between British and American tourism to Greece during the historical periods under examination. Overall, the results of our analysis can provide valuable information for academics and researchers in the field of (Digital) Humanities and Social Sciences, as well as for stakeholders in the tourism industry.

## introduction mass tourism has become a global phenomenon and a $9 trillion industry which, in 2022, accounted for 7.6% of the world's gdp according to the world travel and tourism council. 1 it is vital to the well-being of individuals and nations, and of cultures, economies and societies, proven by the absence of travel during the pandemic and its revival since. tourism is now in question as climate change forces the world to reconsider air miles and ecological footprints. it is more important than ever to understand tourism's history to inform decisions about its future. in this paper, we take greece as a case study and we explore the country's transition to mass tourism from 1945 to 1989 for two reasons. first, it is widely accepted in tourism studies that modern international tourism began in earnest in the 1950s and grew exponentially thereafter, especially with the introduction of affordable package holidays @xcite . in greece in 1950, visitor numbers were c. 33,000. over the next four decades, that figure grew to c. 9 million. we also selected 1989 as an end date for practical research purposes since liberalization of air travel, end of cold war divisions and the usage of the web changed the landscape of tourism and its promotion. there is also a methodological issue. in the second half of the 20th century, the newspapers played a central role in the political, economic and cultural life of britain and the usa. for instance, the british read more newspapers per capita than any other people in the world @xcite . therefore analyzing the debates in these newspapers is an invaluable source of evidence for a wide range of historical investigations and in our case of greek tourism. our focus is american and british newspapers, since the usa and uk were the main markets of tourists throughout this period, along with france and germany. we experimented with lda and nmf to uncover the main topics and narratives framing greek tourism in international newspapers and explore the main textual representations of a tourist nation, their evolution over time, their cultural underpinnings, and how they were communicated to different audiences. the contribution of our work is two-fold; first, the extracted topics are evaluated both by a computational linguist and by a historian highlighting the crucial role of domain experts when interpreting topic modeling outputs. second, the extracted topics are contextualized within the historical and political environment in which they appear, providing interesting insights about the historical representations of greek tourism over the years, and about the development and the hallmarks of american and british tourism in greece across different historical periods @xcite . the comparative analysis between the american and the british press reveals interesting insights including similar responses to specific events as well as notable differences between british and american tourism to greece during the historical periods under examination. overall, the results of our analysis can provide valuable information for academics and researchers in the field of (digital) humanities and social sciences, as well as for stakeholders in the tourism industry. ## background tourism has held a quasi-monopolistic status in greece's development. while the promotion of tourism in greece at times mirrored the agenda of the greek state in the construction of its nation's image during its turbulent history of civil war, dictatorship, and democratization, it has also included the power of the market. after its emergence in the 1960s, almost 80% of greek tourism has been inbound, which exemplifies the industry's exposure to international developments and events. how a nation depicts itself is not a value-free expression of identity, but a culmination of historical processes that 'reveal much about the social construction of space, cultural change, identity and discourse' @xcite . the topic has been researched by visual artists, architects, ethnographers and economic historians of tourism who have looked at governmental archives, hotel/airline and tour operators repositories, posters, official and unsanctioned travel guides and personal travelogues and tourist documentaries and films @xcite @xcite . however, historical methods alone are insufficient for this study. by using computational approaches, it can advance the field of study beyond anthropology, ethnography and sociology to understand the major themes of how greece promoted itself to different audiences, at different times. furthermore, newspapers constitute valuable sources in historical research, providing windows into the past, but also posing challenges for historians to go through their sheer volume of information page-by-page @xcite . recent advancements in the field of historical research have witnessed a growing interest in leveraging topic modeling techniques to capture historical trends (e.g. @xcite and study discourse dynamics diachronically in newspapers collections (e.g. @xcite . in the tourism field, topic modeling is usually applied to travelogues and user comments on travel websites (e.g. @xcite as an important tourist attraction profiling technique facilitating personalized attraction recommendation services @xcite . other approaches focus on online review platforms and social media data to analyze consumer perceptions and (dis)satisfaction of visitors of hospitality and tourism-related products, organizations and services (e.g., @xcite . to the best of our knowledge, topic modeling on historical newspapers has not been employed before in tourism studies. in the field of digital humanities, digital methods have been used to study travel and networks foot_1 , but not the construction, transfer or consumption of a country's tourist destination within its ideological, political and cultural context. ## topic modeling topic models provide an effective way to draw insights from large-scale collections and are widely used in digital humanities and social sciences @xcite to uncover the most prevalent themes on different types of data ranging from books, newspapers, and academic journals to parliament proceedings and social media. since the inception of the term "topic model" along with the introduction of latent dirichlet allocation (lda) by @xcite , topic modeling research has evolved to address the challenges arising from different types of applications, since it became clear that not all algorithms are effective in all types of text @xcite . in this paper, we experiment with lda and non-negative matrix factorization (nmf), two models that are very popular across various domains (e.g. @xcite . the input for the topic models was preprocessed data. all the articles were lowercased, tokenized, part-of-speech tagged and lemmatized. stop words were removed and bigrams were extracted to obtain topics with phrase-like keywords and not only single terms. after several iterations, the stop words list was enriched with more non content terms, and we decided to remove verbs and adverbs and to keep only nouns and adjectives. to find the best hyperparameters for lda, we utilized gensim's foot_2 lda model, focusing on optimizing the number of topics and the alpha parameter. alpha, representing the document-topic density, influences how many topics a document potentially has. we experimented with values like 0.1 (indicating low topic density per document), 'symmetric' (assuming an equal distribution of topics across documents), and 'asymmetric' (allowing for a varied distribution of topics). the number of topics was also varied to identify the optimal structure for our datasets. this approach led us to identify a total of 18 topics in the american articles ranging from 2 to 5 per period, and a total of 24 topics in the british articles ranging from 2 to 7 per period. the results were visualized using pyldavis foot_3 to provide an intuitive understanding of the topics and their distribution. for nmf, we employed the tfidfvectorizer from scikit-learn foot_4 to construct the termsdocuments matrix. this matrix represents the importance of terms in each document, with higher weights assigned to terms that are frequent in a specific document but rare across the entire corpus. to estimate the optimal number of topics, we computed the highest coherence scores for each dataset and sub-corpus using gensim's coherencemodel. this model evaluates the coherence of topics by measuring the degree of semantic similarity between high scoring words in the topic. we identified a total of 39 topics in the american articles ranging from 6 to 9 per period, and a total of 34 topics in the british articles ranging from 3 to 9 per period. the human evaluators concluded that nmf generates more interpretable topics than lda on the specific datasets. a possible reason could be the fact that lda is a static approach, whilst nmf can capture topic evolution in temporal data. another possible explanation could be that, as reported in previous research (e.g. @xcite , nmf performs better than lda on a smaller number of documents. we plan to further explore the differences between the lda and nmf outputs, considering also the unbalanced nature of our datasets (i.e. different number of articles from various newspapers for different periods). in the next section, we discuss findings based on the nmf results. ## results and discussion the topic modeling results for the american articles are presented in appendix a and for the british articles in appendix b. the extracted topics were qualitatively evaluated by a historian and a computational linguist. in this section we discuss the most important findings with the aim to highlight the potential of topic modeling in historical research, and the crucial role of domain experts when interpreting topic modeling outputs. ## conclusions in this paper, we bridge computational linguistics with history with the aim to explore the potential of topic modeling in discovering significant topics and historical trends in newspapers on the representations of greek tourism during the period 1945-1989. overall, the qualitative evaluation of the extracted topics revealed that most of the nmf topics are interpretable. the computational linguist was able to provide descriptive labels (e.g. economy, transportation) to the majority of the topics, while the historian was able to identify more topics that required domain knowledge and also to contextualize them with the historical period under examination. therefore, and in accord with @xcite , we agree with @xcite that "topic simulation is only a tool" and it is essential that an expert in the field contextualizes these topics and evaluates them for relevancy. furthermore, by contextualizing the extracted topics within the historical and political environment in which they appear, we highlighted the role of historical interpretation with regard to analyzing discourse dynamics through topic models. the analysis of our findings provides interesting insights about the main textual representations of greece as a tourist nation, their evolution across different historical periods, their cultural underpinnings, and how they were communicated to different audiences, and offer a tangible example of how computational text analysis can assist historical research.