Stratégie de communication

Résumé

Une stratégie de communication est la manière de planifier et de coordonner la communication. Naissance de la stratégie de communication L’ère industrielle a vu le développement simultané de la production, la distribution et la consommation de masse. La naissance de la publicité au sens moderne du terme date des années 1850 :

1836 : Euloge Winner crée « La Presse » et imagine de financer son journal par des insertions publicitaires et des petites annonces.
En 1841, on assiste à l’apparition des premières agences de publicité qui ne sont encore que des courtiers publicitaires : Volney, Palmer aux États-Unis ; création de la Compagnie Générale d'Annonces de Charles Duveyrier qui s’unit en 1857 à l’agence de presse de Charles Havas.
Aux États-Unis, Bates, Ayer en 1869 et J. Walter Thompson en 1871, annoncent la naissance des premières véritables agences de publicité, intégrant la fonction création.

Les méthodes marketing prennent leur essor dans les marchés de produi

Source officielle

https://fr.wikipedia.org/wiki/Strat%C3%A9gie_de_communication

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In Tags We Trust: Trust modeling in social tagging of multimedia content

Touradj Ebrahimi, Ivan Ivanov, Jong Seok Lee, Péter Vajda

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.

2012

Chargement

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The amount of information that people share on social networks is constantly increasing. People also comment, annotate, and tag their own content (videos, photos, notes, etc.), as well as the content of others. In many cases, the content is tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In such a scenario, however, a wrong or a spam tag can damage the integrity and reliability of the automated propagation system. Users may make mistakes in tagging, or irrelevant tags and content may be added maliciously for advertisement or self-promotion. Therefore, a certain mechanism insuring the trustworthiness of users or published content is needed. In this chapter, we discuss several image retrieval methods based on tags, various approaches to trust modeling and spam protection in social networks, and trust modeling in geotagging systems. We then consider a specific example of automated geotag propagation system that adopts a user trust model. The tag propagation in images relies on the similarity between image content (famous landmarks) and its context (associated geotags). For each tagged image, similar untagged images are found by the robust graph-based object duplicate detection and the known tags are propagated accordingly. The user trust value is estimated based on a social feedback from the users of the photo-sharing system and only tags from trusted users are propagated. This approach demonstrates that a practical tagging system significantly benefits from the intelligent combination of efficient propagation algorithm and a user-centered trust model.

Springer-Verlag2013

Chargement

Spam Fighting in Social Tagging Systems

Touradj Ebrahimi, Ivan Ivanov, Sasan Yazdani

Tagging in online social networks is very popular these days, as it facilitates search and retrieval of diverse resources available online. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and resources may be maliciously added for advertisement or self-promotion. Since filtering spam annotations and spammers is time-consuming if it is done manually, machine learning approaches can be employed to facilitate this process. In this paper, we propose and analyze a set of distinct features based on user behavior in tagging and tags popularity to distinguish between legitimate users and spammers. The effectiveness of the proposed features is demonstrated through a set of experiments on a dataset of social bookmarks.

Springer-Verlag2012

Chargement