The current research explores differences between Facebook, Twitter, Instagram, and Snapchat in terms of intensity of use, time spent daily on the platform, and use motivations. The study applies the uses and gratifications (U&G) approach to contrast the four platforms. A cross-sectional survey of college students (N = 396) asked participants to indicate the intensity of using Facebook, Twitter, Instagram, and Snapchat as well as nine different use motivations. Findings show that participants spent the most time daily on Instagram, followed by Snapchat, Facebook, and Twitter, respectively. They also indicated the highest use intensity for Snapchat and Instagram (nearly equally), followed by Facebook and Twitter, respectively. With regard to use motivations, Snapchat takes the lead in five of the nine motivations. Findings are discussed in relation to the U&G approach and uniqueness of different social media and social networking sites (SNSs).

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Research has shown that family mealtime plays a critical role in establishing good relationships among family members and maintaining their physical and mental health. In particular, regularly eating dinner as a family significantly reduces prevalence of obesity. However, American families with children spend only 1 hour on family meals while three hours watching TV on an average work day. Fine-grained activity-logging is proven effective for increasing self-awareness and motivating people to modify their life styles for improved wellness. This paper presents FamilyLog – a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family through an HMM-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. Our results show that FamilyLog can detect those events with high accuracy across different families and home environments.

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