Online Social Media Analysis and Visualization by Jalal Kawash (eds.)

By Jalal Kawash (eds.)

This edited quantity addresses the large demanding situations of adapting on-line Social Media (OSM) to constructing examine tools and functions. the themes conceal producing lifelike social community topologies, expertise of consumer actions, subject and development new release, estimation of consumer attributes from their social content material, habit detection, mining social content material for universal traits, picking and rating social content material resources, construction friend-comprehension instruments, and so forth. all of the ten chapters take on a number of of those concerns by way of featuring new research tools or new visualization ideas, or either, for well-known OSM purposes akin to Twitter and fb. This selection of contributed chapters handle those demanding situations. on-line Social Media has develop into a part of the day-by-day lives of enormous quantities of hundreds of thousands of clients producing an enormous quantity of 'social content'. Addressing the demanding situations that stem from this huge model of OSM is what makes this booklet a important contribution to the sphere of social networks.

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Online Social Media Analysis and Visualization

This edited quantity addresses the colossal demanding situations of adapting on-line Social Media (OSM) to constructing examine equipment and functions. the subjects hide producing reasonable social community topologies, knowledge of consumer actions, subject and pattern iteration, estimation of consumer attributes from their social content material, habit detection, mining social content material for universal traits, making a choice on and score social content material resources, development friend-comprehension instruments, and so on.

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Multiple descriptions of location that a user lived in the past. From this analysis, we found that most users write something in their profile document but rarely their attributes. This makes it essential to estimate the user attributes. We also found that manual checking of the extraction results is inevitable because applying text matching method to a free-form text tends to yield a lot of noise. We then counted the number of statuses_count and analyzed how many tweets were, on average, available as an estimation resource.

It first finds users who have both Twitter and blog accounts (TwiBlo users) by using the URL described in the url field. It then extracts the attributes that a user specified in his/her blog as true labels of the training data in Twitter about the user. The extraction rules are predefined for each blog site and extraction script outputs user attributes when it is input the URL described in the url field. Table 4 Domain names present in the url field Rank Domain name No. jp 159,768 20,407 20,237 16,500 16,289 Rank Domain name No.

It (ut ) = − log 35 kind(ut ) + α |Ft | . (3) Let u be a user with the user’s profile document up and tweets ut . As indicated by Eq. (1), final estimation probability P is obtained by aggregation of estimation probabilities from the profile document Pp and tweets Pt weighted by reliability scores Rp and Rt , respectively. Rp and Rt are calculated by self-information Ip and It that are obtained by the log ratio of the kind function’s value and the total number of kinds of features |Fp | and |Ft | shown as Eqs.

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