Social media has revolutionized the way in which organizations and consumers interact. Users have adopted massively these channels to engage in conversations about content, products, and brands, while organizations are striving to adapt proactively to the threats and opportunities that this new dynamic environment poses. Social media is a knowledge mine about users, communities, preferences and opinions, which has the potential to impact positively marketing and product development activities.
Social media monitoring tools are being used successfully in a range of domains (including market research, online publishing, etc.). Most of these tools generate its reports from metrics based on volume of posts and on opinion polarity about the subject that is being studied. Although such metrics are good indicators of subject popularity and reputation, these metrics are often inadequate for capturing complex multi-modal dimensions of the subjects to be measured that are relevant to business, and must be complemented with ad-hoc studies such as opinion polls.
The validity of these social metrics depends to a large extent on the population over which they are applied. However, social media users cannot be considered a representative sample until the vast majority of people regularly use social media. Therefore, until then, it is necessary to identify the different strata of users in terms of socio-demographic attributes (e.g., gender, age or geographical precedence), in order to weight their opinions according to the proportion of each stratum in the population. Author and content metadata is not enough for capturing such attributes. As an example, not all the social media channels qualify their users neither with gender nor with geographical location. Some channels, such as Twitter, allow their authors to specify their geographical location via a free text field. However, this text field is often left empty, or filled with ambiguous information (e.g., Paris - France vs. Paris - Texas), or with other data that is useless for obtaining real geographical information (e.g., “Neverland”). For these cases, the friendship networks and the content shared and produced by social media users can be used for estimating their socio-demographic attributes, applying techniques such as geographical entity recognition.
The paper "Characterising social media users by gender and place of residence" explores different techniques for obtaining the place of residence and gender attributes. Such techniques exploit social users’ metadata, the content published and shared by the users to be categorised, and their friendship networks.
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