week 03/1/16 101- Communications
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This 6 page Class Notes was uploaded by tanillemonica on Tuesday March 8, 2016. The Class Notes belongs to 101- Communications at Boston University taught by Tammy vigil in Fall 2016. Since its upload, it has received 29 views. For similar materials see The World of Communication in Journalism and Mass Communications at Boston University.
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Date Created: 03/08/16
Mindful Media Emerging media- Presented by (TA) Sarah Emerging media Developing media and the ideas, behaviors, and communications Emerging media studies A day in the life Merging & blending pop culture, politics, video games, advertising, social media, Overwhelming by being disruptive, disrupted, informative, pervasive, persuasive, disoriented, desensitizing, and repetitive but a sense of community – consuming & participating} and process it Media Literate Taking in/ reading & analyzing media>>Interpreting media as text Think Critical interpretation analysis Feel Do Social &Emotional engagement Creative Production Critical assumptions Media are deliberately created} w/ predetermined message} political & Economical Active recipients of media Plays a role in how we see the world Author & audience Who is intended audience? Who is it created for? What is its purpose? Economics & Consumerism Who or what is being sold? Persuasive tactics used? Representation Who is being represented and who is not being represented? Intertextuality How does the text connect others? Ethics & Responsibility Is how created & shared ethically? What is our ethical responsibility as consumers? As creators? Ideology & bias What ? communicated? Examples: Old Spice commercial } lots of YouTube Views} Interns responded to every comment on behalf of OldSpice More unnoticeable advertisings in plain text by celeb endorsements} now we have to be savvy enough to filter through advertising Transmedia Blending fiction & reality} ex: film & advertising>> Jurassic world The idea of nostalgia by mixing messages Creative production We can consume, engage, & create Ex: music Sir Mix-A-Lot’s baby got back V Nicki Minaj’s anaconda} repurposing the original >>> telling a different POV RECONSTRUCTING media messages (appreciation) ? Rise of Prosumer (Consumers & Producers of Media Content) Text message >> film EX: Every single word- reconstructing & responding to media message How ar ewe users 7 consumers>> we use Facebook & Facebook sell sells our data Posts & share& and advertising Consumers: whatever we share & post Producers: by writing Social & Emotional Connection What is a like- visible support} when engage=sense of community Like=power Algorithm culture: program that then determines your future preface from past likes and associations} tailors content accordingly Big Data What, how, & When Research, study, & analyze effects How media covers certain issues, public opinions, effects what kind and to what extent does media affect, to what extent does social media affect mainstream media Manual content research Analyze media, Scan, study, and analyze media to determine what is the issue that is covered, and its measure Codebook- predetermined categories, ex: themes, economy, and party Research: read, analyze- what is the main issue discussed Intercoder reliability test 2 human coders} both analyze independently: where is the agreement Until understandings & agreement made coders are re-trained, they re-read, and Material content analysis Representative sample- ex: census Big data (DB) - used to understand/guestimate the opinions of a society Data sets that are too big for humans to code a representative Beyond capabilities of traditional use Domain- dependent Data is ever evolving Complitational Social Science (emerging field) Social scientist Computer scientist How Big Data: test advance social science theories Methodological innovation BD CAN NOT REPLACE SMALL DATA} BD=general public opinion in breath but not depth Big Social Data Digital divide: only information collected for BD is those submitted by users on social media -unsolicited, more flexible, biased; digital divide, possible to re-examine past public opinions from archives, and BD is messy 1. Collect big social data 2. Sorting through social media users 3. Analyzing social media users 1.Collecting Application program interface (API) Using existing programs & incorporated into new ones to ease use and saw, time or users while sharing more data between companies Twitter streaming API- free but limited can only supply 1% of all tweets After numbers of tweets exceeds the 1% information= biased #1 API= Python using programing systems to collect data Streaming API Have to prepare to collect data before collect data>>>> if late then data lost Hardware requirements: battery internet connection >>> can never retrieve that information Twitter sells data=$$$ Twitter} is accessible, less privacy restrictions, wide post of extraction Ethical Problems No approval required for research so is it a violation of privacy? 2. Sorting Who is talking? Organization Method 1: analyze user Celebs Method 2: Twitter rest API Not everyone categorizes themselves Method 3: analyze tweets posted by same user Method 4: analyze network statistics *can tell where people are from* Only 1% show location Tweet reveals location 3. Data cleaning Reducing words into base form Prevents misinterpretations= cars becomes just car Remove stop words Remove spaces, punctuation, #s, and special characters Remove sparse words- less than 1% of document Term frequency: top 10 most frequent words Classify documents into known categories Dictionary: based analysis} using related words to categorize Senti- strength- using key words Numerical scale of whether good+ or bad- Human V Senti Supervised machine learning Train Training Mode model Data Data Evaluate Testing model Data Using training data to predict testing data if agrees unsupervised machine working Topic Modeling Detects topics (as a list of words) based on patterns to categorize data DO NOT CONDUCT BIG DATA ANAYLSIS FOR THE SAKE OF BIG DATA BD= breath not depth Misspelling & slang= limitations, ex: Nuances= Limitations, ex: Sarcasm