The connection between social media attachment, tourist flows and overtourism

Alaa Nimer Abukhalifeh; Qingfeng Song

doi:10.1504/ijtp.2021.10040677

The connection between social media attachment, tourist flows and overtourism

International Journal of Tourism Policy ◽

10.1504/ijtp.2021.117370 ◽

2021 ◽

Vol 11 (2) ◽

pp. 117

Author(s):

Qingfeng Song ◽

Alaa Nimer Abukhalifeh

Keyword(s):

Social Media ◽

Tourist Flows

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Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy

Tourism Management ◽

10.1016/j.tourman.2016.06.013 ◽

2016 ◽

Vol 57 ◽

pp. 295-310 ◽

Cited By ~ 71

Author(s):

Alvin Chua ◽

Loris Servillo ◽

Ernesto Marcheggiani ◽

Andrew Vande Moere

Keyword(s):

Social Media ◽

Southern Italy ◽

Social Media Data ◽

Tourist Flows ◽

Media Data

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Tourism destination management using sentiment analysis and geo-location information: a deep learning approach

Information Technology & Tourism ◽

10.1007/s40558-021-00196-4 ◽

2021 ◽

Author(s):

Marina Paolanti ◽

Adriano Mancini ◽

Emanuele Frontoni ◽

Andrea Felicetti ◽

Luca Marinelli ◽

...

Keyword(s):

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Deep Neural Networks ◽

Geographical Location ◽

Word Level ◽

Tourism Sector ◽

Tourist Flows ◽

Media Sentiment

AbstractSentiment analysis on social media such as Twitter is a challenging task given the data characteristics such as the length, spelling errors, abbreviations, and special characters. Social media sentiment analysis is also a fundamental issue with many applications. With particular regard of the tourism sector, where the characterization of fluxes is a vital issue, the sources of geotagged information have already proven to be promising for tourism-related geographic research. The paper introduces an approach to estimate the sentiment related to Cilento’s, a well known tourism venue in Southern Italy. A newly collected dataset of tweets related to tourism is at the base of our method. We aim at demonstrating and testing a deep learning social geodata framework to characterize spatial, temporal and demographic tourist flows across the vast of territory this rural touristic region and along its coasts. We have applied four specially trained Deep Neural Networks to identify and assess the sentiment, two word-level and two character-based, respectively. In contrast to many existing datasets, the actual sentiment carried by texts or hashtags is not automatically assessed in our approach. We manually annotated the whole set to get to a higher dataset quality in terms of accuracy, proving the effectiveness of our method. Moreover, the geographical coding labelling each information, allow for fitting the inferred sentiments with their geographical location, obtaining an even more nuanced content analysis of the semantic meaning.

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Combining Social Media and Mobile Positioning Data in the Analysis of Tourist Flows: A Case Study from Szeged, Hungary

Sustainability ◽

10.3390/su13052926 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2926

Author(s):

Zoltán Kovács ◽

György Vida ◽

Ábel Elekes ◽

Tamás Kovalcsik

Keyword(s):

Social Media ◽

Large Scale ◽

Conceptual Development ◽

Spatial Behavior ◽

Time Data ◽

Traffic Forecasting ◽

Mobile Positioning ◽

Joint Application ◽

Tourist Flows ◽

Tourism Flows

Despite the growing importance of mobile tracking technology in urban planning and traffic forecasting, its utilization in the understanding of the basic laws governing tourist flows remains limited. Knowledge regarding the motivations and spatial behavior of tourists has great potential in sustainable tourism studies. In this paper, we combine social media (Twitter) and mobile positioning data (MPD) in the analysis of international tourism flows in Szeged, a secondary urban center in Hungary. First, the content of geotagged and non-geotagged Twitter messages referring to Szeged in a six-month period of 2018 was analyzed. In this way specific events attracting foreign tourists were identified. Then, using MPD data of foreign SIM cards, visitor peaks in the investigated period were defined. With the joint application of the social media and mobile positioning analytical tools, we were able to identify those attractions (festivals, sport and cultural events, etc.) that generated significant tourism arrivals in the city. Furthermore, using the mixed-method approach we were also able to analyze the movements of foreign visitors during one large-scale tourism event and evaluate its hinterland. Overall, this study supports the idea that social media data should be combined with other real-time data sources, such as MPD, in order to gain a more precise understanding of the behavior of tourists. The proposed analytical tool can contribute to methodological and conceptual development in the field, and information gained by its application can positively influence not only tourism management and planning but also tourism marketing and placemaking.

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Where Social Media Meets AAC

ASHA Leader ◽

10.1044/leader.gs.20072015.np ◽

2015 ◽

Vol 20 (7) ◽

Author(s):

Vicki Clarke

Keyword(s):

Social Media

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Social Media: A Powerful Tool for AAC Learning and Networking

ASHA Leader ◽

10.1044/leader.gs.22022017.np ◽

2017 ◽

Vol 22 (2) ◽

Author(s):

Lauren Enders

Keyword(s):

Social Media

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Making Twitter Work For You

ASHA Leader ◽

10.1044/leader.stw.18052013.np ◽

2013 ◽

Vol 18 (5) ◽

Keyword(s):

Social Media ◽

Public Relations ◽

Speech Language Pathologists

As professionals who recognize and value the power and important of communications, audiologists and speech-language pathologists are perfectly positioned to leverage social media for public relations.

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