Download How TV Advertising and Social Network Help Tobacco Control

How TV Advertising and Social Network
Help Tobacco Control Campaigns Influence More
Qianyi Zhan
National Laboratory
for Novel Software Technology,
Nanjing University, Nanjing, China
[email protected]
Sherry Emery
University of Illinois at Chicago,
Chicago, IL, USA
[email protected]
Abstract
The influence of new social media on health behaviors
has been well established. In this paper, we focus on
social network activities related to tobacco control advertisement campaigns. We aim to find out how advertising is related to the social media conversation, and
to what extent the social conversation stimulates further
engagement with the campaign. Three methods of measurement are used to solve this problem. Among them
a novel inference model: SII model is proposed, which
can predict whether user will attend the conversation.
The results of all methods shows TV exposures information launches the social conversation and the diffusion process inside the social network further stimulates
the engagement with the campaign.
The media landscape has been changing rapidly and dramatically over the past 10 years. Whether it takes the form of
social marketing or traditional advertising, the influence of
media on health behaviors has been well established. For example, anti-tobacco mass media campaigns have been found
to be associated with reductions in tobacco use, while drug
prevention campaigns were related to decreased risk perceptions and increased likelihood of substance use (Daubresse
et al., 2015; Emery et al., 2005, 2012). Further, our research
has shown that health-related advertising generally works:
televised ads for products ranging from electronic cigarettes,
to prescription drugs for cessation, asthma and arthritis are
associated with greater sales volume and use of these products (Kim, 2015; Pagani and Mirabello, 2011). In the new
media environment, social media platforms play two roles:
they can provide initial exposure to the messagevia a tweet
or facebook message that mentions the campaign and may
provide a link to the online version of an ad; in this role,
social media messages can amplify the effect of exposure to
advertising, to gain a larger audience. Second, social media
can provide a forum for commentary, interpretation, and/or
expression of behavioral intentions related to the message
of the ad; in this role, social media users can provide campaigns with important feedback on perceived effectiveness
of an ad.
Little is known however, about how advertising is related
to the social media conversation, and to what extent the
Copyright c 2015, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
Philip S. Yu
University of Illinois at Chicago,
Chicago, IL, USA
Institute for Data Science,
Tsinghua University, China
[email protected]
social conversation stimulates further engagement with the
campaign. In this paper, we examine the propagation, or diffusion, of information about two different health campaigns,
focusing on understanding how much messaging is generated from traditional (TV advertising) versus social conversation (Tweets) about each campaign. We propose three
methods to measure the influence of TV advertising and all
results illustrate TV advertising makes a great impact on activities in social networks.
This paper is organized as follows: a description of measurement and analytic methods; an outline of analytic framework; presentation of two case studies, using television ratings and twitter data from two different anti-smoking campaigns; summary of results.
Methods of Measurement
In this section, to identity what launches social conversation
about a specific topic, we introduce three methods of measurement: statistics measurement, source measurement and
the inference model.
Statistics measurement
In statistics, Pearson Correlation Coefficient (PPMC) is a
measure of the linear correlation between two variables and
Spearman rank correlation coefficient assesses how well the
relationship between two variables can be described using
a monotonic function. We conduct both Pearson and Spearman correlation tests to measure the relationship between
the number of related tweets and TV rating, an index indicating how many people have watched this advertising.
Source measurement
This measure can tell us how quickly and from where users
are influenced. We define a time window which represents a
time duration before user being activated. For example user
u posted a tweet t at 3pm and if the length of time window is 2 hours, time window of b is 1pm to 3pm. TV set
Sutv collects TV advertising in this time window. Similarly,
tweets set Susn gathers tweets from u’s friend. Exposure set
Su = Sutv [ Susn contains all exposures related to the campaign. We change the length of time window and examine
whether users can get information from TV, tweets and either way during this time window, i.e., whether Sutv , Susn and
Su is not empty.
Table 1: Twitter Statistic Summary
Inference model
We also develop a novel model: Social Influence Inference
(SII) Model, which is proposed to predict users who will get
infected to join the social conversation related to a specific
ad. We estimate TV advertising’s social influence by incorporating the public media effect created by TV broadcasting and social media effect from user to user through social
links.
The existing information diffusion models which take external events into consideration are proposed for news and
popular social trends (Lin et al., 2013; Myers, Zhu, and
Leskovec, 2012). However these models cannot be applied
directly on TV advertising, because the aim of advertising
is different and user feelings evoked by advertising is more
complicated. Since the consumer attitude has been extensively researched in psychology and marketing area, our SII
model is designed based on classical model mentioned in
both social psychology and marketing theories. (Fiske and
Gilbert, 2010) The theory defines that user attitude has three
stages: Cognitive, Affective and Conative. We modify these
stages to fit our case and explain them in detail as following.
• Cognitive: At first audiences become product aware. In
the SII model, this stage represents that users gather
knowledge from TV and tweet.
• Affective: This stage ensures target consumers liking the
product or audiences having strong feelings on the advertising. In the SII model, affective means that users are
deeply touched by TV and SN appearances.
• Conative: On this stage, audiences have tendency to take
action toward the campaign. In the viral marketing, the action is defined as posting tweets related to this campaign.
The advertising information spread through TV exposures
and tweet exposures and each exposure influence audience
according to the above three stages. At last impressive exposures are aggregated to activate users taking social actions.
We learn the parameters from training data and predict whether a user will be influenced. Mean squared error
(MSE) which reports the average of the squares deviation of
predictions to the ground truth data, is used to measure the
correctness of the prediction.
Case Study
In this section, we describe two real-world TV advertising
campaigns of tobacco control in detail. Health Media Collaboratory of UIC1 is funded to conduct evaluations of these
campaigns and the evaluations are designed to draw upon
data from Twitter network to characterize the social conversation about the campaign.
The first campaign is “Tips from Former Smokers 2013”,
hereinafter to be referred as “CDC Tips”, launched by Centers for Disease Control and Prevention (CDC). The other
campaign is “Truth” launched by American Legacy Foundation (Legacy), which is a Washington, D.C.-based national
public health organization devoted to tobacco-use prevention and education. We call this campaign “Legacy Truth”
for short in the following parts.
1
http://www.healthmediacollaboratory.org/
Date
Twitter
Retweets
Users
Tweets per Users
Edges
Followers Median
Followers Max
CDC Tips
Mar. 4 - Jun. 23
146,759
46,402
126,327
1.162
76,916
331
2,853,320
Legacy Truth
Aug. 1 - Oct. 31
59,605
45,676
47,852
1.246
30,275
480
14,857,309
CDC Tips
“CDC Tips” was the federal government’s first nationwide
effort to use paid advertising to prevent smoking and encourage quitting. The Tips 2013 campaign began at March
4 and ended at June 23 and contained 10 stories from the
former smokers. Besides the main placement strategy: TV
advertising, the CDC also placed ads in print publications,
outdoor venues and radio.
Overall, the “CDC Tips” campaign generated a total of
146,759 tweets related to the televised advertisements, an
average of 1,277 tweets per day. The statistics summary of
tweets collected over the duration of the campaign (Mar. 1Jun. 23) is listed in the “CDC Tips” column of Table 1. We
use the three measurements mentioned above to check the
influence from TV advertising and social links.
Statistics Results
The total number of tweets (146,759) was summed daily
over the duration of the “CDC Tips” campaign from March
1 to June 23 (115 days). Fig. 1(a) and 1(b) shows the results
of both correlation tests for the entire campaign and creative
themes. Both the Pearson correlation coefficient (0.64) and
Spearman rank correlation (0.83) report a strong positive relationship between ratings and tweets. Among all the topics
of “CDC Tips”, Terrie exhibited the strongest correlation in
both the Pearson correlation coefficient (0.64) and Spearman
rank correlation (0.80). Jessica and Suzy had lesser correlations but still had very strong relationships.
Source Results
We calculate the proportion of users can get information
from TV, tweets and either way. The statistic results with
different time windows are shown in Fig. 1(c), which counters the intuition that people will tweet as soon as they see
these exposures. When the length of time window is 1 hour,
more than 70% of users cannot receive any kind of exposure. Therefore the probability of tweeting behavior happens immediately (less than 1 hour) is much below 0.3. Until extending the time window to 12 hours, most (93.2%) of
users can get campaign message. While even forward tracing 3 days, only quarter (25.4%) users can see tweets from
their friends, which indicates the social network constructed
by tweeting users’ is very sparse. This may because “CDC
Tips” did not do much online marketing in Twitter network.
Inference Results
The prediction results are shown in Fig. 1(d), which with
the training ratio increasing, the accuracy of prediction im-
proves. The proposed SII model consistently outperforms
other methods which indicates the advertising campaign’s
social conversation is launched by both TV and social links.
The prediction based on only TV outperforms the result of
only tweets greatly demonstrates that TV broadcasting plays
a significant role in ads information propagation.
Legacy Truth
“Legacy Truth” provides young people with facts and information about the health and social consequences of tobacco,
and empowers the teens generation to finish smoking. As
“Legacy Truth” advertises all year around, we intercepted
the record during Aug. 11 to Oct. 28, 2013. “Legacy Truth”
payed much attention on TV advertising and televised their
ads during the 2013 MTV Video Music Awards. At the same
time it adopted a social media strategy that cultivates and encourages engagement. It promoted some specific hashtags,
encouraged social conversation, and required some celebrities to join the social activities.
“Legacy Truth” column in Table 1 lists this campaign generated a total of 59, 605 related tweets during three months,
an average of 647.88 tweets per day. Significantly, 76.6%
tweets are generated by retweeting, which is a proof that
users’ network is less sparser. Like “CDC Tips”, we find the
answers to the same two questions.
Statistics Results
Like the analysis of “CDC Tips”, we also measure the
Pearson and Spearman correlation between TV rating and
tweets amount of “Legacy Truth”. The result is shown in
Fig. 2(a) and 2(b), where TV rating reached a high peak on
Aug, 24 since “Legacy Truth” ads were aired during 2013
MTV Video Music Awards. Moreover tweets amount also
rose dramatically and peaked at 28, 958 on Aug, 25 because
of much more viewers and some music stars started discussing “#Truth” in Twitter at the same time. The Pearson
correlation coefficient (0.48) and Spearman rank correlation
(0.79) demonstrate that the TV rating and tweets amount
have strong co-relation. Since there is an extreme situation
in this data, which makes the relation in other normal days
not obvious, we move out the 10 days data (Aug. 24- Sept.
2). The even higher correlation Pearson correlation coefficient (0.62) proves the strong relation still exists and Fig.
2(b) also illustrates it.
Source Results
We analyze “Legacy Truth” with the same analysis setting
to observe user twitter behavior. Fig. 2(c) shows the proportion of tweets’ authors can be influenced by TV and social
media exposures with different time windows. Unlike “CDC
Tips”, most users (75.1%) can receive campaign message in
1 hour, because of intense TV advertising. “Legacy Truth”
also enjoys high probability of users influenced by social
friends which is the result that campaign made an effort on
viral marketing. However, it is interesting that even for 3 day
time window, two ways of spreading information still cannot
cover all users, which means a small part of users (2.5%) are
activated through other channels.
Inference Results
Fig. 2(d) shows the prediction results, which the proposed
SII model predict better than one resource model. This result
supports the conclusion of “CDC Tips”: Both TV advertising and social media influence the social conversations. But
the better prediction result based on only TV demonstrates
that more tweeting users are influenced by TV ads than social diffusion. This conclusion is in agreement with Fig. 2(a),
in which twitter amount is highly correlated with TV rating.
Conclusion
In this paper, we aim to identify the role of TV advertising and social media in launching social conversation about
tobacco control. We proposed a novel inference model: SII
model, which can predict whether user will attend the conversation. With other two methods of measurement, we
check the interaction of TV advertising and social networks
activities. The results of all methods shows both the external
TV exposures information and the diffusion process inside
the social network stimulates the engagement with the campaign, and TV advertising plays a more important role.
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Tweets
Nielsen Rating
9,000
Tweets
60
26
24
50
30000
22
45
35
5,000
30
4,000
25
20
3,000
20
25000
20000
14
12
15000
10
15
2,000
18
16
Tweets
40
Nielsen Rating
6,000
8
10000
6
10
1,000
4
5000
5
2
0
0
1-Mar
11-Mar
21-Mar
31-Mar
10-Apr
20-Apr
30-Apr
10-May
20-May
30-May
9-Jun
0
19-Jun
0
1-Aug
Date
Bill
Buerger's Disease
Cessation Tips
Jessica
Marie
Michael
Nathan
Roosevelt
Suzy
Terrie
Tiffany
11-Aug
21-Aug
31-Aug
10-Sep
30-Sep
10-Oct
20-Oct
30-Oct
(a) Correlations between all Tweets Amount and TV rating
Tweets
9,000
20-Sep
Date
(a) Correlations between all Tweets Amount and TV rating
Tweets
Nielsen Rating
7,000
Tweets
Nielsen Rating
35000
55
8,000
30
Nielsen Rating
600
25
28
8,000
26
500
24
7,000
20
16
14
4,000
12
10
3,000
10
200
8
2,000
15
300
Nielsen Rating
Tweets
18
5,000
400
Tweets
20
6,000
Nielsen Rating
22
6
4
1,000
5
100
2
0
1-Mar
0
11-Mar
21-Mar
31-Mar
10-Apr
20-Apr
30-Apr
10-May
20-May
30-May
9-Jun
0
19-Jun
1-Aug
Date
(b) Correlations between Tweets Amount and TV rating with Different topics
Either
TV
11-Aug
21-Aug
11-Sep
21-Sep
1-Oct
11-Oct
0
31-Oct
21-Oct
Date
(b) Correlations between part of Tweets Amount and TV rating
Either
Tweets
TV
Tweets
1
1
0.8
Proportion
Proportion
0.8
0.6
0.4
0.6
0.4
0.2
0.2
0
0
1h
48h
Time Window
(c) Proportion of users who can get exposures with different time
windows
4h
8h
12h
24h
32h
40h
48h
72h
TV
Tweet
1
(d) Mean squared error of prediction with different train data ratio
Figure 1: Measure results of “CDC Tips”
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1790
0.2306
0.1
0.1807
0.2312
0.9
0.1826
0.2315
0.2185
0.2376
0.8
0.1818
0.2334
0.2191
0.2383
0.7
0.1762
0.2348
0.2193
0.2385
0.6
TRAIN RATE
0.1795
0.2331
0.2183
0.2374
0.5
0.1823
0.2318
0.2189
0.2381
0.4
0.1851
0.2304
0.2200
0.2392
0.3
0.1891
0.2274
0.2237
0.2433
0.2
0.1962
0.2244
0.2235
0.2430
0.1
0.2181
0.2372
0.2288
0.2485
MSE
MSE
0.9271
0.9273
0.9323
20h
(c) Proportion of users who can get exposures with different time
windows
ALL
0.9241
0.9251
16h
Time Window
Tweet
0.9258
TV
0.9269
0.9298
0.9288
0.9292
ALL
2h
72h
0.9900
40h
0.9900
32h
0.9900
24h
0.9900
20h
0.9900
16h
0.9900
12h
0.9900
8h
0.9900
4h
0.9900
2h
0.9900
1h
1
TRAIN RATE
(d) Mean squared error of prediction with different train data ratio
Figure 2: Measure results of “Legacy Truth”