01931nas a2200265   4500000000100000000000100001008004100002260001200043653002200055653001800077653003200095653003900127653002200166100002300188700003100211700003400242700001600276700001800292245013800310856008300448300000900531490001300540520109800553022001401651       9998                            d  c10/202310aDestination Image10aDeep Learning10aNatural Language Processing10aDestination Marketing Organization10aScene Recognition1 aAngel Diaz-Pacheco1 aMiguel A. Álvarez-Carmona1 aAnsel Y. Rodríguez-González1 aHugo Carlos1 aRamón Aranda00aMeasuring the Difference Between Pictures From Controlled and Uncontrolled Sources to Promote a Destination. A Deep Learning Approach  uhttps://www.ijimai.org/journal/sites/default/files/2023-10/ip2023_10_003_0.pdf  a1-140 vIn press3 aPromoting a destination is a major task for Destination Marketing Organizations (DMOs). Although DMOs control, to some extent, the information presented to travelers (controlled sources), there are other different sources of information (uncontrolled sources) that could project an unfavorable image of the destination. Measuring differences between information sources would help design strategies to mitigate negative factors. In this way, we propose a deep learning-based approach to automatically measure the changes between images from controlled and uncontrolled information sources. Our approach exempts experts from the time-consuming task of assessing enormous quantities of pictures to track changes. To our best knowledge, this work is the first work that focuses on this issue using technological paradigms. Notwithstanding this, our approach paves novel pathways to acquire strategic insights that can be harnessed for the augmentation of destination development, the refinement of recommendation systems, the analysis of online travel reviews, and myriad other pertinent domains.  a1989-1660