Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: A set of language tags in a backend application can be passed to a frontend application in the form of a language selection tool. The disclosure creates a mapping between the frontend and backend application of at least one non-default language descriptor string onto a language tag of the backend application. The frontend language selection tool then displays to the user the non-default language descriptor string. The use of non-default language descriptor strings clarifies to the user otherwise ambiguous language identifiers.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: A set of language tags in a backend application can be passed to a frontend application in the form of a language selection tool. The disclosure creates a mapping between the frontend and backend application of at least one non-default language descriptor string onto a language tag of the backend application. The frontend language selection tool then displays to the user the non-default language descriptor string. The use of non-default language descriptor strings clarifies to the user otherwise ambiguous language identifiers.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: Social networking applications may be improved by incorporating a user's language proficiency to make content suggestions to the user. A language preference of a user, which may represent one of a plurality of signals, may be received. A signal may be, for example, an online activity of the user, a text generated or received by the user, or content requested by the user. At least one of the plurality of signals may be analyzed using a machine learning program. A machine learning program may be trained on data for a test group of users with a known language proficiency. A user-assigned language proficiency may be incorporated as a signal in training a machine learning program. The language proficiency of the user may be determined based upon the analysis of at least one of the plurality of signals. Content may be presented to the user based upon the language proficiency of the user.

Abstract: Social networking applications may provide an enhanced user experience by considering information regarding the language preference of a user as well as other signals. According to the implementations disclosed herein, one or more signals may be utilized to determine the language in which content may be displayed to a user of a social networking application or website. A first language preference for a user may be determined based on at least one signal. A second language preference may be determined for a social graph component of the social networking application based on at least one signal. The first language preference may be compared to the second language preference and a weighted value may be assigned to the social graph component. The social graph component of the social networking application may be presented based upon the weighted value assigned to the social graph component.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: Techniques for determining one or more preferred languages for a user are provided. The preferred languages may be determined based upon a set of language indicators. The language indicators are analyzed using, for example, rules-based techniques, clustering, language classifiers, and the like, or combinations thereof. Language indicators can include or be derived from information about the user's behavior, location, preferences, social connections, or other data related to the user.

Abstract: A process for a language selection using one or more language preferences is disclosed. Information for presenting a language preference selection control may be sent to a user. A language preference may be received from the user and stored to a server. A cookie, which includes an indication of the language preference, may be caused to be stored to a device of a user. The stored language preference may be accessed from the cookie responsive to a communication from the user. Responsive to whether the user has logged into an account, the language preference may be retrieved from either the server or from the cookie. The retrieved language preference may be compared with languages available in an application and a most preferred language for the application may be determined based upon the comparison. Information from the application may be provided to the user in the most preferred language.

Abstract: An ordered list of language indicators may be obtained, wherein each of the language indicators is either user defined or system defined. Each language indicator may be ranked according to a priority of the language indicator for determining a most-preferred language for a user. A list of languages may be received that indicates the languages available in an application. The ordered list of language indicators may then be compared to the received languages, and a highest-ranked language indicator determined. The most-preferred language for the user may be selected based upon the highest-ranked language indicator. Information from the application may then be provided to the user in the most preferred language.