Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Social Media Marketing and Insights > Designing Content I > Flashcards

Designing Content I Flashcards

1
Q

Content marketing is

A

a form of non-traditional marketing communications whereby a brand produces or designs content in various forms (e.g., text, images, video, audio)
and disseminates that content to targeted audiences and/or customers

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

T/F

Content marketing is NOT new

A

T

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Content marketing emphasizes the creation of

A

relevant and valuable online content to drive

business results

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Features of Content Marketing

A

An integral part of social media marketing, SEO, PR strategies

ROI may be difficult to quantify

Attribution problem under the the multi-channel multi-touch environment
 $, frequency, reach of content, can be easily
measured

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Content features including formats…

A

…links, promotional nature, topics,

messages, quality of writing, creativity, humor …

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

__ __ __ brings unique opportunities to content marketing research

A

Unstructured big data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

CONTENT IS __ BIG DATA

A

UNSTRUCTED

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

The 4 “V”s of Big Data

A

Volume
Veracity
Variety
Velocity

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Unstructured data will account for more than __ of the data collected by organizations.

A

80%

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Unstructured data (or unstructured information) is

A

information that either does not have a pre-defined data model or is not organized in a pre-defined
manner.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Unstructured data files often include

A

text and multimedia content, examples include e-mail messages, word processing documents, videos, photos, audio files, presentations, webpages and many other kinds of documents.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Unstructured data are not new–previous forms:

A

 Qualitative research (e.g., in-depth interviews, focus groups, open-ended
questions in consumer surveys)

 Qualitative analysis attempts to reduce the vast amount of verbal or observational
data to a set of well-defined and clearly explained patterns and themes

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

unstructured big data opportunities and challenges

A

 Need tools to extract useful information from unstructured data in a scalable
way

 Find related social media posts (for example,
using Twitter API to extract data)
 Sentiment analysis on text
 Generate word cloud to summarize the text

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Text Mining

 What to mine?

A 
 Online reviews
 Social media posts
 News
 Entertainment products(e.g., movie scripts, books)
 Brand/product descriptions
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Text Mining

 Purpose?

A

 Sentiment analysis
 Measuring consumer preferences/motivation
 Understanding and assisting the creation of
creative content

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Text Mining Process

A

Collect Data

Preprocessing

Applying text mining techniques

Analysis of text

Discovery of knowledge

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Collect Data

A
  • Documents
  • Webpages
  • Online Reviews
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Preprocessing

A
  • Tokenizing (bag-of-words)
  • Tagging parts-of-speech
  • Filtering
  • Stemming
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Applying text mining techniques:

A
  • Keyword methods: LIWC, SentiWordNet, etc.

* Statistical methods: LDA, SVM, Naïve Bayes, etc.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Analysis of text

A
  • Sentiment analysis
  • Topic modeling
  • Language style
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Why focus content marketing on social

media?

A 
 Control (over paid and owned media)
 Cost
 Audience engagement
 Virality
 Feedback (earned media)
22
Q

How Does Content Drive Business

Results?

A

Exposure
Influence
Engagement
Action

23
Q

Schweidel and Moe (2014) demonstrates online venues

(e.g., blogs, forums, social networks, micro-blogs) differ in:

A

 Extent of social interaction
 Amount of information
 Audience attracted
 Focal product/attributes
 Develop a joint model of brand sentiment and venue format
choice
 Unstructured data: sentiment and topics are manually coded

24
Q

Results show “significant variation in average sentiment across venue formats, highlighting

A

the importance of separating the effects of venue format from any sentiment measure”

25
Q

Tucker (2015) tackles the question

A

whether “popular” contents are effective contents in driving sales

26
Q

Crowd sourced persuasiveness measure

A

“by randomly exposing half of these consumers to a video ad and half to a similar placebo video ad, and then surveying their attitudes towards the focal product”

27
Q

Video ads designed to be “viral” are in general

A

less persuasive

28
Q

relative ad persuasiveness is on average

A

10% lower for every one million views that the video ads achieve

29
Q

Exceptions are ads that

A

generated views and large number of comments, and ads that attracted comments that mentioned the product by name.

These ads tend to be perceived as “funny” rather than
“outrageous”.

30
Q

Li and Xie (2020): studies the relationship between

A

imagery content and social media engagement (likes and shares) using Twitter and Instagram data

31
Q

Theoretic Framework

A

Imagery Content

Text Content

Control variables:

32
Q

Imagery Content

A
  • Mere Presence
  • Image characteristics:
    • Colorfulness
    • Human face and emotional state
    • Image Source
    • Image Quality
33
Q

Text Content

A 
• Sentiment
• Topic
• Linguistic Content Category
• Behavioral Drivers
• Number of Hashtags (#), Mentions,
(@), Emojis, and Words
34
Q

Control variables:

A
  • Posting Time

* Account Characteristics

35
Q

Li and Xie (2020) Research Findings

A

 Processing unstructured data
SVM to exact topics and sentiment from text content
 Google vision API and manual coding to extract information from
imagery content
 Manual coding to determine the relevancy between text content and
imagery content
Modeling: zero-inflated bivariate negative binomial model

36
Q

How to make people pay attention and engage with your content? What should we do to figure out the type of content (e.g., content features) that make people pay attention and engage with your content

A

Lab or field experiment

37
Q

Component of an Experiment

A

(At least) one independent variable
 Independent variable is what experiment
manipulates

(At least) one dependent variable
 Dependent variable is what researcher is interested
in explaining

(At least) one manipulation
 Independent variables are manipulated in some
systematic way

38
Q

(__ __) causes a change in (__ __) and it
isn’t possible that (__ __) could cause a change in
(__ __).

A

Independent variable

dependent variable

dependent variable

independent variable

39
Q

A/B testing

A

one independent variable with no more than two levels or aspects and one manipulation

40
Q

Factorial design requires us to

A

figure out potential interactions between independent variables

41
Q

Factorial Designs

A

Allows for manipulation of two or more independent variables
at the same time
Each variable has two or more levels or aspects
Allow to test main effects as well as interactions.

42
Q

A main effect in Factorial Designs

A

is the separate influence of each IV on the DV

43
Q

Interaction occurs (in Factorial Designs) when

A

the simultaneous effect of two or more IVs is different from the sum of their independent effects.

44
Q

Using observational data

A

Start with your own social audit: Go back in the past and look at everything you posted on your
various social media accounts in the last 3-6 months

Conduct a social audit on your competitors: Look at everything they posted on various social media accounts in the last 3-6 months and ask the same questions

45
Q

Independent variables (things you control)

A

 Content features
 Posting time
 Promoted or not
 Posting platform

46
Q

Dependent variables (feedback received)

A 
 Views
 Likes
 Shares
 Comments: counts, sentiment, topics
 Engagement rates
47
Q

Construct a regression model to

A

figure out the type of content (e.g., content features) that tend to receive higher engagement
 Helps us predict the engagement level of a specific
post

48
Q

Correlation:

A

measuring the closeness of the relationship or

joint variation between two variables

49
Q

Regression:

A

derive an equation that relates the dependent variable to one or more independent variables
 Correlation ≠ Causation

50
Q

Correlation only measures

A

the nature and degree of association or co-variation between variables.

51
Q

Estimation method:

A

Ordinary least squares (OLS).

Parameters are estimated from the sample data so that the sum of squared errors is minimized.

Social Media Marketing and Insights (23 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions