User:Hbaranowski/sandbox

Bibliography

Bousquet, Chris. “Mining Social Media Data for Policing, the Ethical Way.” Data, 26 Apr. 2018, datasmart.ash.harvard.edu/news/article/mining-social-media-data-policing-ethical-way.

McCourt, Abby. “Social Media Mining: The Effects of Big Data In the Age of Social Media.” Yale Law School, law.yale.edu/mfia/case-disclosed/social-media-mining-effects-big-data-age-social-media.

Drafting assignment: (Instructions on drafting!)


 * 1) Open the article you want to change in Edit mode. (References and other templates will break if you copy from Read mode.)
 * 2) Select the portion you want to work on — a few paragraphs at most — and copy it.
 * 3) Open your sandbox in Edit mode and paste the copied article content.
 * 4) Add an edit summary that says copied from   with the name of the original article, then save it by clicking Publish changes.
 * 5) Re-enter Edit mode in your sandbox, make your changes, and publish them.

Uses[edit]
Social media mining is used across several industries including business development, social science research, health services, and educational purposes. Once the data received goes through social media analytics, it can then be applied to these various fields. Often, companies use the patterns of connectivity that pervade social networks, such as assortativity—the social similarity between users that are induced by influence, homophily, and reciprocity and transitivity. These forces are then measured via statistical analysis of the nodes and connections between these nodes. Social analytics also uses sentiment analysis, because social media users often relay positive or negative sentiment in their posts. This provides important social information about users' emotions on specific topics.

These three patterns have several uses beyond pure analysis. For example, influence can be used to determine the most influential user in a particular network. Companies would be interested in this information in order to decide who they may hire for influencer marketing. These influencers are determined by recognition, activity generation, and novelty—three requirements that can be measured through the data mined from these sites. Analysts also value measures of homophily: the tendency of two similar individuals to become friends. Users have begun to rely on information of other users' opinions in order to understand diverse subject matter. These analyses can also help create recommendations for individuals in a tailored capacity. By measuring influence and homophily, online and offline companies are able to suggest specific products for individuals consumers, and groups of consumers. Social media networks can use this information themselves to suggest to their users possible friends to add, pages to follow, and accounts to interact with.

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To me, this article details the uses of social media mining, but does not touch at all into the ethical and legal implications that should be discussed when dealing with social media mining. In the drafting section, I will touch more on that.