Sosiaalisen median vaikutukset matkakohteen valintaan, esimerkkinä Suomi

Tiivistelmä. Sosiaalinen media ja matkailu ovat maailmalla kasvavia trendejä, ja niiden rooli vahvistuu ihmisten elämissä vuosi vuodelta. Tämä kirjallisuuskatsaus perehtyy sosiaalisen median ja matkakohteen valinnan väliseen suhteeseen. Sosiaalinen media voidaan määritellä avoimena, osallistavana ja ihmisten väliseen kanssakäymiseen sekä keskusteluun kohdistuvana yhteisönä. Sosiaalisen median erilaisten palveluiden kautta, kuluttajat voivat luoda yhteisöjä ja pitää yhteyttä niin muihin kuluttajiin kuin palveluntarjoajiin. Sosiaalisen median palveluissa tapahtuu markkinointia sekä yritysten kuin yksilöidenkin toimesta. Markkinoinnille sosiaalinen media mahdollistaa kuluttajan ja yrityksen välistä keskustelua. Muun muassa palautteeseen ja kysymyksiin, voi saada vastauksia sosiaalisen median kautta jopa reaaliajassa. Matkailumarkkinointia tapahtuu valtioiden virallisten matkailumarkkinoijien, matkailuyritysten ja yksilöiden toimesta. Yksityishenkilöt jakavat omia matkailukuviaan sosiaalisen median verkostopalveluissa. Heidän käyttäessä muun muassa hashtageja ja paikkamerkintöjä, myös muut kyseisestä matkakohteesta kiinnostuneet kuluttajat näkevät tämän sisällön. Viralliset matkailumarkkinoijat markkinoivat matkakohteita niiden vetovoimatekijöiden avulla sopiville kohderyhmille. Lappia markkinoidaan esimerkiksi sen eksoottisen luonnon perusteella, kun taas pääkaupunkiseutua arkkitehtuurin avulla. Matkailumarkkinoinnin määrä lisääntyy sosiaalisessa mediassa koko ajan, ja niin myös sosiaalisen median vaikutus matkakohteen valintaan. Kirjallisuuskatsaus sijoittuu matkailumaantieteen kenttään, käsitellen pääosin matkailuun liittyvää sosiaalisia jataloudellisia ulottuvuuksia. Lisäksi sekä sosiaalista mediaa että matkakohdetta voidaan käsitellä paikkoina, joka tuo tutkimusaiheen myös lähemmäs ihmismaantieteen kenttää

Raumgeographische Verteilung von Twitter-Hashtags im deutschen Sprachraum

Diese Studie untersucht die räumliche Verteilung von Hashtags in einem Korpus deutschsprachiger Tweets unter Berücksichtigung dreier Arten von Nutzerstandortinformationen: exakter Standort, kodiert als Breitengrad-Längengrad-Koordinaten, ein „place“-Attribut, ausgewählt aus einer von Twitter geführten Liste von Orten, oder ein freier Eintrag im Nutzerprofil. Hashtags in Tweets mit exakten Ortsangaben weisen mit etwas höherer Wahrscheinlichkeit eine räumliche Konzentration auf als Hashtags mit Orts- oder Nutzerangaben, was möglicherweise auf die Verwendung von Mobilgeräten zur Veröffentlichung von Tweets zurückzuführen ist. Die Analyse der räumlichen Autokorrelation zeigt zwar, dass die meisten Hashtags keine starke räumliche Tendenz aufweisen, aber bei denjenigen, bei denen dies der Fall ist, handelt es sich meistens um Toponyme, Appellativa oder Eigennamen, die mit bestimmten Orten in Verbindung gebracht werden, wie eine auf Kartierung der Autokorrelationswerte veranschaulicht. Darüber hinaus beschreiben einige Hashtags, die eine räumliche Tendenz aufweisen, lokalisierte geografische oder meteorologische Phänomene.This study examines the spatial distribution of hashtags in a corpus of German-language tweets by considering three kinds of user location information: exact location encoded as latitude-longitude coordinates, a „place“ attribute selected from a Twitter-maintained list of places, or a free-form entry in the user profile. Hashtags in tweets with exact locations are slightly more likely to show spatial concentration, compared to hashtags with place or user location information, which may reflect the use of mobile devices to publish tweets. While spatial autocorrelation analysis shows that most hashtags do not exhibit a strong spatial tendency, those that do are likely to be toponyms, appellatives, or proper nouns associated with specific places, as can be shown by mapping autocorrelation values. In addition, some hashtags that exhibit a spatial tendency describe localized geographical or meteorological phenomena

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.Comment: In Proc. CHI 2018 Papers program. Please cite: El Ali, A., Stratmann, T., Park, S., Sch\"oning, J., Heuten, W. & Boll, S. (2018). Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA. DOI: https://doi.org/10.1145/3173574.317413

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.Comment: In Proc. CHI 2018 Papers program. Please cite: El Ali, A., Stratmann, T., Park, S., Sch\"oning, J., Heuten, W. & Boll, S. (2018). Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, New York, NY, USA. DOI: https://doi.org/10.1145/3173574.317413

Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization

This paper extends recent research into the usefulness of volunteered photos for land cover extraction, and investigates whether this usefulness can be automatically assessed by an easily accessible, off-the-shelf neural network pre-trained on a variety of scene characteristics. Geo-tagged photographs are sometimes presented to volunteers as part of a game which requires them to extract relevant facts about land use. The challenge is to select the most relevant photographs in order to most efficiently extract the useful information while maintaining the engagement and interests of volunteers. By repurposing an existing network which had been trained on an extensive library of potentially relevant features, we can quickly carry out initial assessments of the general value of this approach, pick out especially salient features, and identify focus areas for future neural network training and development. We compare two approaches to extract land cover information from the network: a simple post hoc weighting approach accessible to non-technical audiences and a more complex decision tree approach that involves training on domain-specific features of interest. Both approaches had reasonable success in characterizing human influence within a scene when identifying the land use types (as classified by Urban Atlas) present within a buffer around the photograph’s location. This work identifies important limitations and opportunities for using volunteered photographs as follows: (1) the false precision of a photograph’s location is less useful for identifying on-the-spot land cover than the information it can give on neighbouring combinations of land cover; (2) ground-acquired photographs, interpreted by a neural network, can supplement plan view imagery by identifying features which will never be discernible from above; (3) when dealing with contexts where there are very few exemplars of particular classes, an independent a posteriori weighting of existing scene attributes and categories can buffer against over-specificity

Linking geosocial sensing with the socio-demographic fabric of smart cities

Technological advances have enabled new sources of geoinformation, such as geosocial media, and have supported the propagation of the concept of smart cities. This paper argues that a city cannot be smart without citizens in the loop, and that a geosocial sensor might be one component to achieve that. First, we need to better understand which facets of urban life could be detected by a geosocial sensor, and how to calibrate it. This requires replicable studies that foster longitudinal and comparative research. Consequently, this paper examines the relationship between geosocial media content and socio-demographic census data for a global city, London, at two administrative levels. It aims for a transparent study design to encourage replication, using Term Frequency—Inverse Document Frequency of keywords, rule-based and word-embedding sentiment analysis, and local cluster analysis. The findings of limited links between geosocial media content and socio-demographic characteristics support earlier critiques on the utility of geosocial media for smart city planning purposes. The paper concludes that passive listening to publicly available geosocial media, in contrast to pro-active engagement with citizens, seems of limited use to understand and improve urban quality of life

Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data

Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context. To investigate the limitations of standard Internet data collection methods in low- and middle-income countries, we analyze human-centric image geo-diversity on a massive scale using geotagged Flickr images associated with each nation in Africa. We report the quantity and content of available data with comparisons to population-matched nations in Europe as well as the distribution of data according to fine-grained intra-national wealth estimates. Temporal analyses are performed at two-year intervals to expose emerging data trends. Furthermore, we present findings for an ``othering'' phenomenon as evidenced by a substantial number of images from Africa being taken by non-local photographers. The results of our study suggest that further work is required to capture image data representative of African people and their environments and, ultimately, to improve the applicability of computer vision models in a global context.Comment: 35 pages, AI, Ethics, and Society Conference (AIES'23

Datenerhebung mit neuer Informationstechnologie: Empfehlungen zu Datenqualität und -management, Forschungsethik und Datenschutz

Die Datenerhebung mit neuer Informationstechnologie - also mit Smartphones, Wearables und anderen Sensoren - eröffnet der Wissenschaft ungeahnte Potenziale. Sensoren können Daten z.B. zu Aufenthaltsorten, Bewegungen, Geräuschen, Lichtverhältnissen, Medien-Nutzung, Video- und Sprachaufnahmen im Alltag und in Echtzeit erfassen. Wiederholte Datenerfassungen werden ebenso vereinfacht. Mit dieser Handreichung skizziert der Rat für Sozial- und Wirtschaftsdaten (RatSWD) eine qualitätssichernde Rahmung der Nutzung neuer Informationstechnologie in der Forschung