Abstract
Unwanted content in online social network services is a substantial issue that is continuously growing and negatively affecting the user-browsing experience. Current practices do not provide personalized solutions that meet each individual’s needs and preferences. Therefore, there is a potential demand to provide each user with a personalized level of protection against what he/she perceives as unwanted content. Thus, this paper proposes a personalized filtering model, which we named SentiFilter. It is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics. An experiment involving 80,098 Twitter messages from 32 users was conducted to evaluate the effectiveness of the SentiFilter model. The effectiveness was measured in terms of the consistency between the implicit feedback derived from the SentiFilter model towards five selected topics and the explicit feedback collected explicitly from participants towards the same topics. Results reveal that commenting behavior is more effective than liking behavior to detect unwanted content because of its high consistency with users’ explicit feedback. Findings also indicate that sentiment of users’ comments does not reflect users’ perception of unwanted content. The results of implicit feedback derived from the SentiFilter model accurately agree with users’ explicit feedback by the indication of the low statistical significance difference between the two sets. The proposed model is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.
Highlights
Online Social Network (OSN) services provide online and instant communication in a large-scale manner
The results obtained from the two phases of the experiment are demonstrated in two forms: the effectiveness of the sentiment-based classifier, and the comparison between the implicit feedbacks derived from of the SentiFilter model against the users‟ explicit feedback
Personalization is a fundamental task in most aspects of our daily life
Summary
Online Social Network (OSN) services provide online and instant communication in a large-scale manner. Despite the great social experience and communication benefits of these services, the vast usage of OSN services increases the amount of user-generated content, which brings several challenges and concerns regarding privacy, data management, information filtering, and content moderation. Users of such services are exposed to various kinds of content that can be unwanted or harmful [1]. Solving the spam issue requires taking into consideration several aspects in order to propose solutions. These aspects include type of spam, where to detect spam, the form of spam, and how to detect it
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