What Do Employers Expect for Jobs Requiring Media Analytics? A Semantic Network Analysis of Job Descriptions of In-Person and Remote Positions During the COVID-19 Pandemic

Abstract

Using text mining and semantic network analysis, this study analyzed the job descriptions of 34,787 positions about media analytics from Indeed.com to compare how the in-person and remote jobs differ to inform educators about integrating analytics in the media and communications curriculum. We found that the in-person positions emphasized more on the skills of verbal, interpersonal, and organizational communication, whereas the remote positions asked more for written communication. While the in-person positions had higher expectations of using general data management and analysis tools, the remote positions emphasized more on the use of social media analytics and digital marketing tools.

Keywords

media analytics remote in-person semantic network analysis

Due to media digitization, a large amount of data about media users and how they consume media content have become more accessible (Kapatamoyo, 2019). Media analytics has thus integrated into the functioning of media and communications professions to drive daily decision-making (Hollifield, 2020). It has become an increasingly sought ability for new college graduates seeking media and communication jobs (Adams & Lee, 2021; Freberg & Kim, 2018). Educators have called attention to integrate media analytics into undergraduate curriculum in media and communications (Neill & Schauster, 2015). However, the lack of clarification on the roles and responsibilities of media analytics makes it challenging for media and communication educators to figure out the industry expectations and prepare undergraduates with relevant skills (Stansberry & MacKenzie, 2020). To address this challenge, this study analyzes the description of job postings with the keyword of media analytics.

Due to the outbreak of the COVID-19 pandemic, many jobs have transitioned to remote work since Spring 2020, which provides us a unique context to compare how the expectations of the in-person and the remote positions about media analytics differ. This remote working trend is said to extend beyond the pandemic at least partially if not to the full extent (Coate, 2021; Parker et al., 2020). Therefore, this study chooses to situate the analysis of job descriptions in the context of COVID-19 to better understand the industry expectations and gain insights into how to prepare our students for both the in-person and remote positions of media analytics. Specifically, this study aims to uncover the differences between the in-person and remote media analytics positions regarding job location, job title, employer type, and how they describe expected skills and required tools through text mining and semantic network analysis.

Analytics and Media Analytics

The term analytics has been used in different ways and in different contexts. While some definitions highlight its quantitative rigor (Cooper, 2012; Daniel, 2015; Tandoc, 2019), others underline its goal-driven nature (Hawkins, 2008; Hollifield, 2020). Following Dinsmore (2016), this study considers analytics as the process of developing useful insights from data for problem-solving. Media analytics focuses on the data about media users and media content (Manovich, 2018). In media and communication industries, analytics includes a wide range of practices, such as general media monitoring, social media listening, evaluation of advertising and marketing effectiveness, assessment of user experience, and distribution forecasting. (Granados, 2019; PwC, n.d.). Despite the differences in purpose, these analytical practices all deal with data about either users or how they consume media and media content. Therefore, this study defines media analytics as the process and practice of gathering and analyzing data about both media users and media content for problem-solving. This problem-solving orientation requires media analytics to go beyond statistical analysis to include the components of asking the right questions, identifying data to answer the questions, gaining actionable insights from data, and effectively presenting insights (Grady, 2020; Hollifield, 2020). With the proliferation of web and social media, more communication positions are particularly looking for employees with media analytics skills to understand the data of user interaction with online and interactive media (Meng et al., 2019).

Data Competence and Analytics in Media and Communication Education

According to a recent study released by the Plank Center for Leadership in Public Relations, communication professionals in North America have identified large gaps between perceived importance and personal qualification level of data, technology, and management competencies, with data as the weakest area of developed competence (Meng et al., 2021). Compared with older professionals, younger professionals were found to be particularly lagging in skills and knowledge about data (Meng et al., 2021). Data competence is critical to media analytics. It addresses the ability to conduct analysis of data about media users and content as well as the knowledge and practices about research methods, data collection, and interpretation of results. Media analytics also require technology competence, which facilitates the learning and use of relevant data tools.

To meet the increasing needs of new hires with data and technology competences, educators in media and communications have called for an integration of analytics at both the program and the course levels (Adams & Lee, 2021). For example, Commission on Public Relations Education (CPRE, 2018) has identified analytics as one of the most important topics for undergraduate public relations curriculum. Accrediting Council on Education in Journalism and Mass Communications (ACEJMC, 2012) has also emphasized the assessment of students’ ability to conduct research and evaluate information with appropriate methods and apply basic numerical and statistical concepts.

However, universities have been slow in integrating analytics into curriculum. O’Boyle and Sturgill (2020) found that most programs of ACEJMC-accredited universities did not offer any analytics-focused course, with only about half offering some courses with analytics components. The programs with a dedicated focus on media analytics are mainly at the graduate level (O’Boyle & Sturgill, 2020). The lack of clarification on the role and responsibilities of media analytics makes it challenging for media and communication educators to figure out how to prepare and integrate analytics into the undergraduate curriculum (Stansberry & MacKenzie, 2020). To help educators understand what is expected from an industry perspective, this study examines the descriptions of jobs requiring media analytics on Indeed.com. It is important to note that the positions requiring media analytics are not just limited to media industries. Other industries provide the relevant positions too. Therefore, in addition to studying the skills and tools described in the job ads, this study also examines the location, the title, and the employer type of these jobs.

Rise of Remote Work During the COVID-19 Pandemic

The outbreak of the COVID-19 pandemic ushered many offices and workplaces to pivot to remote work in spring 2020. By May 2020, more than one-third of employees in United States reported to work from home during the past 4 weeks due to COVID-19 (Coate, 2021). Among them, employees in management, business, and professional occupations had a higher likelihood to work remotely than others (Coate, 2021). Pew Research Center reported that employees with bachelor’s degree or higher were more likely to report that their work could be done at home than those without a college degree (Parker et al., 2020). Shifting to remote work was also more commonly found for people with more education during the pandemic (Bartik et al., 2020).

Internship, the important career launching pad for college students, has also been significantly influenced by the COVID-19 pandemic. During the peak of the pandemic in 2020, around half of all internship opportunities had been canceled (Martin, 2021), making the demand for internship opportunities far exceed the available positions (Hora et al., 2021). Despite the plunge in overall internship opportunities, remote internships have increased since the start of this pandemic (Feldman, 2021). A recent report from Indeed Hiring Lab showed that the share of all internship postings on its U.S. website dropped 39% compared with the year before the pandemic, while the share of remote internships went up almost seven times in 2020 (Konkel, 2021).

Several of the most common fields with emerging remote jobs and internships, such as marketing, social media, and arts and entertainment (Konkel, 2021; Lusinski & Ward, 2020), are highly relevant to students majoring in media and communications. It is important to understand the similarities and differences between the in-person and the remote positions requiring media analytics. Therefore, this study proposes the following research question.

RQ1: What are the differences and similarities between the in-person and the remote positions requiring media analytics regarding (a) location, (b) job title, and (c) employer type?

By analyzing the expected skills and mastery of tools listed in job ads, this study also informs the development and revision of media and communications curriculum to better prepare students for the new shifts in the job market.

RQ2: What are the differences and similarities between the in-person and remote positions requiring media analytics regarding the skills mentioned in job descriptions?

RQ3: What are the differences and similarities between the in-person and remote positions requiring media analytics regarding the tools mentioned in job descriptions?

Semantic Networks of Job Descriptions

Aside from the mentions of skills and tools, this study further compares the differences between in-person and remote positions by analyzing the semantic networks of job descriptions. Semantic network analysis (SMA) is a form of content analysis identifying the network of associations between words expressed in a text (Doerfel, 1998). This method assumes that the text represents a network of words. The position of concepts within a text network thus “provides insight into the meaning or prominent themes of the text as a whole” (Hunter, 2014, p. 350). Its theoretical foundation rests on the cognitive paradigm (D’Angelo, 2002) and the tradition of frame semantics in linguistics (e.g., Fillmore, 2008). Collins and Quillian (1972) argue that words are hierarchically clustered in memory. Thus, the spatial models illustrating the relations among words are representative of meaning (Barnett & Woelfel, 1988). Through examining the co-occurrence of words in texts, we can identify salient words in specified concept clusters and explain the related framing strategies. SMA has been applied to media and communications scholarship in various areas, such as issue framing (e.g., Jiang et al., 2016), public opinions (e.g., Kwon et al., 2016), and crisis management (e.g., Liu et al., 2018). This study uses SMA to examine and visualize the associations between mentioned skills and tools in job descriptions as well as salient skills or tools in specified concept clusters.

The comparison between the sematic networks of the in-person and the remote positions is conducted through the analyses of Quadratic Assignment Procedure (QAP) correlation, modularity, and centrality. QAP correlation (Borgatti et al., 2002) is a nonparametric technique that does not rely on the assumption of independence. A higher correlation suggests a higher level of similarity in structure between two networks. Modularity analysis addresses how words in a network are clustered into groups (Blondel et al., 2008). It calculates how the mentioned skills and tools are grouped into smaller communities in each semantic network. Together, QAP correlations and modularity analyses help answer the following question about the structural differences in semantic networks.

RQ4: How do the semantic networks of the in-person and remote positions differ from each other regarding (a) network structure and (b) number of clusters?

Each word within a semantic network could have different levels of influence on the network, which is measured through normalized eigenvector centrality (Bonacich, 1972; Freeman, 1978). It is important to examine the overall salience of a particular skill or tool mentioned in each cluster of the semantic networks of in-person and remote positions.

RQ5: What were the most central skills and/or tools in each cluster of the two semantic networks?

Method

Using “media analytics” as the search string, this study identified 34,787 jobs posted on Indeed.com (one of the largest job sites) from May 19, 2020, to January 11, 2021. Among them, more than 25% (n = 7464) were remote positions. The web scraping service parsehub.com was used to collect information about job title, location, employer, and job description. Based on the online database of Dun & Bradstreet (https://www.dnb.com/business-directory/company-search.html), we identified the employer type for the top 100 employers that appeared most frequently.

The text corpus was divided into the in-person and remote positions. The tidytext package for R was used to clean the textual data by removing the stop words that are typically extremely common words in English (e.g., “the,” “of,” “to,” etc.). The most frequently used phrases identified in job descriptions, such as “marketing strategies” and “social media strategies,” were coded as one concept. Frequency analysis in texting mining was used to answer RQ1 to RQ3.

Semantic networks of job descriptions were created based on the bigrams of mentioned skills and tools. QAP correlation, modularity, and normalized eigenvector centrality were calculated to explore the correlation between the two semantic networks and to identify the clusters within each network and the salient skills and/or tools in each cluster (RQ4 and RQ5). The ForceAtlas2 layout in Gephi (Jacomy et al., 2014) was used to create visual maps of semantic networks (Figures 1 and 2) to supplement the discussions of modularity and centrality results.

Figure 1.

Semantic Network of Skills and Tools in Job Descriptions of Media Analytics In-Person Positions.

Figure 2.

Semantic Network of Skills and Tools in Job Descriptions of Media Analytics Remote Positions.

Results

Top Job Locations, Job Titles, and Employer Types (RQ1)

Both the in-person and remote jobs were mainly from the metropolitan areas with the highest economic outputs, especially along the East and West Coast. Table 1 lists the top 20 cities with the greatest numbers of in-person and remote positions, respectively. Among them, New York City had the most in-person and remote positions. While the ratios of remote to in-person positions were relatively lower in Charlotte, Seattle, San Francisco, Chicago, and New York City; Washington, D.C., Portland, Los Angeles, Houston, and Nashville had relatively higher ratios of remote to in-person positions.

Table 1.

Top Locations With the Greatest Number of Media Analytics Jobs.

	In-Person			Remote
Rank	City	F	R	City	F	R
1	New York, NY	1,973	0.19	New York, NY	470	0.19
2	Chicago, IL	795	0.18	Los Angeles, CA	257	0.32
3	San Francisco, CA	664	0.18	Washington, DC	201	0.39
4	Los Angeles, CA	555	0.32	Austin, TX	194	0.27
5	Austin, TX	530	0.27	Atlanta, GA	189	0.27
6	Atlanta, GA	512	0.27	Chicago, IL	178	0.18
7	Boston, MA	400	0.23	San Francisco, CA	145	0.18
8	Seattle, WA	391	0.17	Boston, MA	122	0.23
9	Charlotte, NC	338	0.14	Denver, CO	107	0.29
10	Washington, DC	318	0.39	San Diego, CA	100	0.25
11	San Diego, CA	305	0.25	Dallas, TX	98	0.27
12	Denver, CO	267	0.29	Philadelphia, PA	93	0.34
13	Dallas, TX	265	0.27	Seattle, WA	79	0.17
14	Tampa, FL	206	0.26	Miami, FL	76	0.29
15	Miami, FL	186	0.29	Houston, TX	72	0.30
16	Philadelphia, PA	184	0.34	Tampa, FL	72	0.26
17	Columbus, OH	176	0.09	Minneapolis, MN	65	0.28
18	Houston, TX	169	0.30	Nashville, TN	60	0.30
19	Minneapolis, MN	164	0.28	Portland, OR	60	0.38
20	Orlando, FL	161	0.25	Charlotte, NC	56	0.14

Note. F represents the frequency of cities appeared on the job postings. R is the ratio of remote to in-person jobs in these cities.

As shown in Table 2, the top 20 most frequently mentioned job titles of both in-person and remote positions requiring media analytics are related to marketing or social media. Marketing manager, digital marketing specialist, and digital marketing manager emerged as the three most frequently mentioned job titles for both in-person and remote positions. Compared with the in-person positions, the remote positions involved more opportunities for internships.

Table 2.

Top 20 Most Frequent Job Titles for the Media Analytics Jobs.

	In-Person		Remote
	Job title	F	Job title	F
1	Marketing Manager	776	Digital Marketing Specialist	264
2	Digital Marketing Specialist	736	Marketing Manager	242
3	Digital Marketing Manager	650	Digital Marketing Manager	230
4	Marketing Coordinator	588	Social Media Manager	160
5	Marketing Specialist	403	Marketing Coordinator	156
6	Social Media Manager	396	Social Media Intern	104
7	Social Media Specialist	318	Marketing Specialist	95
8	Marketing Assistant	245	Social Media Specialist	76
9	Marketing Analyst	235	Social Media Coordinator	75
10	Social Media Coordinator	221	Marketing Intern	71
11	Marketing Director	198	Marketing Assistant	63
12	Director of Marketing	190	Marketing Director	57
13	Marketing Associate	167	Digital Marketing Account Manager	55
14	Digital Marketing Coordinator	163	Digital Marketing Coordinator	49
15	Product Marketing Manager	156	Marketing Associate	46
16	Marketing Intern	151	Director of Marketing	44
17	Digital Marketing Analyst	118	Content Marketing Manager	40
18	Digital Marketing Strategist	101	Product Marketing Manager	40
19	Content Marketing Manager	95	Digital Marketing Intern	39
20	Email Marketing Specialist	91	Social Media Marketing Intern	39

Note. F represents the frequency of job titles appeared on the job postings.

Among the top 20 employer types offering the greatest number of positions (Table 3), e-commerce employers offered the most in-person positions requiring media analytics, followed by employers in wireless & telecommunications, advertising & marketing services, staffing & recruiting, bank & credit unions, information technology services, pharmaceutical manufacturing, and social media. Employers from advertising & marketing services offered the greatest number of remote positions requiring media analytics, followed by employers in staffing and recruiting, insurance carriers, agencies and brokerages, information technology services, education and training services, e-commerce, and publishing.

Table 3.

Top 20 Most Frequent Employer Types for the Media Analytics Jobs.

In-Person		Remote
Employer types	F	Employer types	F
E-commerce	516	Advertising & Marketing Services	411
Wireless Telecommunications	421	Staffing & Recruiting	161
Advertising & Marketing Services	396	Insurance Carriers, Agencies & Brokerages	85
Staffing & Recruiting	396	Information Technology Services	74
Banks & Credit Unions	327	Education & Training Services	43
Information Technology Services	265	E-Commerce	34
Pharmaceutical Manufacturing	242	Publishing	34
Social Media	214	Consulting Services	22
Insurance Carriers, Agencies & Brokerages	189	Wireless Telecommunications	22
Computer Software	162	Social Assistance	20
TV Production, Broadcast & Cable Networks	153	Computer Software	15
Nursing Homes & Long-Term Care Facilities	77	Sci. & Tech. Instruments Manufacturing	15
Scientific & Technical Instruments Manufacturing	73	Manufacturing Sector	14
Publishing	59	Scientific Research & Development Services	14
Colleges & Universities	57	Radio Broadcasting & Programming	13
Managed Application	57	Managed Application	12
Consulting Services	50	Computer Manufacturing	11
Medical Equipment & Supplies Manufacturing	49	Discount Department Stores	11
Physicians	45	Lending	11
Real estate	40	Personal care products manufacturing	11

Note. F represents the frequency of employer types for in-person and remote positions.

Most Frequently Mentioned Skills (RQ2)

To explore how in-person and remote positions requiring media analytics differ regarding expected skills, we identified a total of 89 skills and calculated their mention frequencies respectively. Table 4 lists the top 50 most frequently mentioned skills as well as their percentage of frequency among the 89 identified skills. As shown in Table 4, both in-person and remote positions highly emphasized the skills of communication, writing, research, collaboration, and development of marketing strategies.

Table 4.

Top 50 Most Frequent Skills in Job Descriptions of Media Analytics Positions.

	In-Person	F	P	Remote	F	P
1	Communication	44,015	19.51	Communication	7566	17.02
2	Writing	18,540	8.22	Writing	3770	8.48
3	Research	14,564	6.46	Research	2,767	6.22
4	Collaboration	11,141	4.94	Collaboration	2,411	5.42
5	Marketing strategies	7,549	3.35	Marketing strategies	1,570	3.53
6	Verbal	6,346	2.81	Content creation	1,312	2.95
7	Project management	6,008	2.66	Verbal	1,168	2.63
8	Organizational	6,001	2.66	Marketing campaigns	1,147	2.58
9	Marketing campaigns	5,428	2.41	Organizational	1,075	2.42
10	Content creation	5,071	2.25	Email marketing	1,056	2.38
11	Marketing automation	4,192	1.86	Project management	1,021	2.30
12	Interpersonal	4,083	1.81	Marketing automation	912	2.05
13	Email marketing	3,927	1.74	Social media platforms	825	1.86
14	Presentation	3,752	1.66	Content marketing	731	1.64
15	Marketing plans	3,695	1.64	Social media marketing	679	1.53
16	Social media platforms	3,060	1.36	Interpersonal	619	1.39
17	Marketing experience	2,666	1.18	Html	593	1.33
18	Html	2,519	1.12	Presentation	593	1.33
19	Oral	2,471	1.10	Marketing plans	564	1.27
20	Content marketing	2,386	1.06	Team player	516	1.16
21	Team player	2,369	1.05	Marketing experience	480	1.08
22	Analytics	2,360	1.05	Brand awareness	475	1.07
23	Brand awareness	2,248	1.00	Marketing channels	467	1.05
24	Customer experience	2,156	0.96	Online marketing	448	1.01
25	Marketing channels	2,035	0.90	Time management	435	0.98
26	Time management	1,998	0.89	Content management	385	0.87
27	Content management	1,921	0.85	Email campaigns	384	0.86
28	Social media marketing	1,904	0.84	Marketing management	372	0.84
29	Programming	1,820	0.81	Content strategy	371	0.83
30	Marketing communication	1,784	0.79	CSS	360	0.81
31	Business development	1,759	0.78	Social media accounts	357	0.80
32	Marketing management	1,748	0.77	Social media strategies	352	0.79
33	Social media channels	1,739	0.77	Oral	351	0.79
34	Content strategy	1,633	0.72	Social media management	351	0.79
35	Online marketing	1,614	0.72	Social media channels	333	0.75
36	CSS	1,516	0.67	Business development	313	0.70
37	Email campaigns	1,492	0.66	Customer experience	307	0.69
38	User experience	1,395	0.62	Advertising campaigns	297	0.67
39	Digital channels	1,389	0.62	User experience	297	0.67
40	Social media strategies	1,365	0.61	Digital advertising	293	0.66
41	Digital advertising	1,308	0.58	Media buying	290	0.65
42	Digital marketing campaigns	1,291	0.57	Social media content	284	0.64
43	Advertising campaigns	1,288	0.57	Digital marketing campaigns	267	0.60
44	Management experience	1,180	0.52	Analytics	249	0.56
45	Digital content	1,171	0.52	Digital channels	240	0.54
46	Product development	1,171	0.52	Programming	240	0.54
47	Social media management	1,163	0.52	Marketing communication	233	0.52
48	Business intelligence	1,126	0.50	Social media campaigns	225	0.51
49	Social media content	1,112	0.49	Data science	200	0.45
50	Data science	1,105	0.49	Strategy development	197	0.44

Note. F represents the frequency of mentioned skills for in-person and remote positions. P represents the percentage of frequency among the identified skills (n = 90).

Compared with remote positions, the job descriptions of in-person positions had a higher percentage of frequency for the skills about communication and presentation, analytics, marketing plans, project and brand management, programming, customer experience, research, product development, management, business intelligence, digital content, data visualization, and business development. In contrast, the job descriptions of remote positions had a higher percentage of frequency for the skills related to content (e.g., content creation, content strategy, and content marketing), social media (e.g., the platform itself, marketing, management, and strategies, and analytics of social media), email marketing and campaigns, marketing strategies, and campaigns in general, collaboration and teamwork, writing, time management, and web development (e.g., HTML and CSS).

Most Frequently Mentioned Tools (RQ3)

After identifying 42 tools mentioned in job descriptions, we calculated their mention frequencies by in-person and remote positions. As shown in Table 5, both the in-person and the remote positions emphasized Google Analytics and Facebook the most. Excel and Instagram were the third most frequently mentioned tools for in-person and remote positions, respectively. Compared with remote positions, in-person positions placed more emphasis on the tools for content creation and communication, such as Microsoft Office, PowerPoint, Word, Adobe Creative, and InDesign, as well as for data management and analysis, such as SQL, Tableau, Salesforce, Python, SAS, Adobe Analytics, Nielsen, ComScore, SPSS, and Kenshoo.

Table 5.

Frequency of Mentioned Tools in Job Descriptions of Media Analytics Positions.

	In-Person	F	P	Remote	F	P
1	Google Analytics	7,850	10.81	Google Analytics	2,254	12.91
2	Facebook	7,251	9.99	Facebook	2,076	11.89
3	Excel	6,906	9.51	Instagram	1,321	7.56
4	Salesforce	4,096	5.64	Google Ads	1,147	6.57
5	Instagram	4,092	5.64	Excel	1,109	6.35
6	Twitter	3,925	5.41	Twitter	992	5.68
7	Google ads	3,526	4.86	LinkedIn	956	5.47
8	Word	3,302	4.55	Salesforce	845	4.84
9	PowerPoint	3,256	4.48	YouTube	720	4.12
10	Microsoft office	3,120	4.30	Facebook Page	659	3.77
11	LinkedIn	3,092	4.26	Word	575	3.29
12	YouTube	2,688	3.70	Photoshop	498	2.85
13	SQL	2,536	3.49	Microsoft Office	487	2.79
14	Photoshop	2,075	2.86	PowerPoint	467	2.67
15	Tableau	1,943	2.68	Pinterest	376	2.15
16	Adobe Creative	1,671	2.30	SQL	357	2.04
17	Pinterest	1,328	1.83	Facebook Ads	322	1.84
18	Illustrator	1,123	1.55	Adobe Creative	319	1.83
19	Python	1,058	1.46	Illustrator	258	1.48
20	InDesign	1,022	1.41	Tableau	239	1.37
21	Facebook Ads	903	1.24	InDesign	204	1.17
22	Adobe Analytics	659	0.91	Google Tag	198	1.13
23	Hootsuite	610	0.84	Snapchat	168	0.96
24	Facebook Page	599	0.82	Hootsuite	160	0.92
25	Snapchat	585	0.81	Python	144	0.82
26	SAS	557	0.77	Adobe Analytics	95	0.54
27	Google Tag	531	0.73	Premiere	94	0.54
28	Nielsen	490	0.67	Nielsen	60	0.34
29	Premiere	375	0.52	Facebook Insights	53	0.30
30	ComScore	222	0.31	Sprinklr	51	0.29
31	Sprinklr	207	0.29	Instagram Ads	50	0.29
32	Facebook insights	154	0.21	SAS	37	0.21
33	SPSS	143	0.20	Display Network	33	0.19
34	Microsoft Suite	135	0.19	YouTube Ads	29	0.17
35	Instagram Ads	132	0.18	Twitter Ads	24	0.14
36	Kenshoo	123	0.17	Microsoft Suite	20	0.11
37	Display Network	99	0.14	Brandwatch	17	0.10
38	Twitter Ads	81	0.11	Twitter Analytics	17	0.10
39	Twitter Analytics	66	0.09	Kenshoo	11	0.06
40	Brandwatch	47	0.06	SPSS	11	0.06
41	Sysomos	21	0.03	ComScore	8	0.05
42	YouTube Ads	14	0.02	Sysomos	3	0.02

Note. F represents the frequency of mentioned tools for in-person and remote positions. P represents the percentage of frequency among the identified tools (n = 42).

In contrast, the job descriptions of remote positions placed more emphasis on the specific online and social media platforms and the relevant tools, such as Facebook, Facebook Page, Facebook Ads, and Facebook Insights, Instagram and Instagram Ads, Google Ads and Google Tag, LinkedIn, YouTube and YouTube Ads, Pinterest, Twitter, Snapchat, and Hootsuite.

Differences in Semantic Networks (RQ4 and RQ5)

QAP Correlation and Network Modularity (RQ4a and 4b)

The QAP correlation result showed that the semantic network structures of in-person and remote positions were similar to each other (r = .96, p < .001). The modularity results further confirmed this finding. The modularity scores of the in-person and the remote semantic networks were 0.666 and 0.631 respectively, which indicated dense connections between the skills and the tools within the same clusters and sparse connections between the skills and the tools across different clusters in each semantic network. There were seven clusters in the in-person network and six in the remote one (Table 6).

Table 6.

QAP Correlation and Modularity Analysis Results.

In-Person			Remote			QAP: 0.96
Modularity	.666		Modularity	.631		(p = .000)
Clusters	P (%)	Hub	Clusters	P (%)	Hub
Red	42.64	Research	Red	36.44	Google Analytics
Blue	21.71	Google Analytics	Blue	34.75	Research
Green	10.85	Communication	Green	11.86	Facebook
Purple	10.85	Facebook	Purple	8.47	SQL
Orange	6.20	Excel	Orange	5.08	Excel
LBlue	4.65	HTML	LBlue	3.39	Photoshop
LGreen	1	Photoshop

Note. P represents the percentage of each cluster in the semantic network. Hub represents the most salient skills or tool in each cluster in terms of eigenvector centrality. LBlue = Light Blue; LGreen = Light Green; QAP = quadratic assignment procedure.

Most Salient Skill and/or Tool in Each Cluster (RQ5)

Table 7 lists the normalized eigenvector centralities (ranging from 0 to 1, with 1 representing the greatest centrality) and modularity class of each skill and tool in the two semantic networks. For the in-person positions (Figure 1), the largest clusters covered 42.64% of the network and centered around the skill of research closely related to the skills of content creation and collaboration. The second largest cluster (21.71%) centered around the tool Google Analytics closely tied to a variety of digital advertising and marketing tools, such as Google Ads, Facebook Ads, Twitter Ads, and Salesforce. Communication was the hub of the third largest cluster (10.85%), which was closely associated with skills of writing, verbal, oral, interpersonal, and organizational communication as well as presentation. Facebook was the hub of another third largest cluster (10.85%), which was closely tied to other specific social media platforms, such as Instagram, Twitter, LinkedIn, YouTube, and Pinterest. This social media cluster was also closely connected to the skills in managing social media channels, accounts, and pages, as well as increasing social media presence across diverse platforms. Excel was the hub of the fifth largest cluster (6.2%) and was strongly associated with the tools of Word, PowerPoint, and Microsoft Office. The sixth largest cluster centered around the skill of HTML (4.65%), which was closely tied to the skills about CSS, basic HTML, and HTML coding. Photoshop was the hub of the smallest cluster (1%) and was strongly tied to InDesign, Illustrator, and Premiere.

Table 7.

Eigenvector Centralities and Modularity Class of Nodes in the Two Semantic Networks.

	In-Person	Eigen	Cluster	Remote	Eigen	Cluster
1	Google Analytics	1.000	Blue	Google Analytics	1.000	Red
2	Communication	0.955	Green	Facebook	0.817	Green
3	Research	0.859	Red	Research	0.814	Blue
4	Email Marketing	0.854	Red	Google Ads	0.666	Red
5	Writing	0.793	Green	Email marketing	0.653	Red
6	Project management	0.783	Green	Writing	0.646	Blue
7	Google Ads	0.757	Blue	LinkedIn	0.587	Green
8	Content creation	0.731	Red	Communication	0.538	Blue
9	Facebook	0.670	Purple	Content creation	0.533	Blue
10	Marketing automation	0.649	Red	Twitter	0.525	Green
11	Social media platforms	0.595	Purple	Social media platforms	0.509	Green
12	Salesforce	0.583	Blue	Instagram	0.484	Green
13	LinkedIn	0.579	Purple	Salesforce	0.450	Red
14	YouTube	0.571	Purple	YouTube	0.434	Green
15	Collaboration	0.560	Red	Project management	0.433	Blue
16	Excel	0.552	Orange	Social media management	0.416	Red
17	Digital advertising	0.536	Red	Facebook Ads	0.406	Red
18	Social media management	0.519	Red	Excel	0.401	Orange
19	Content marketing	0.516	Red	Marketing strategies	0.400	Blue
20	PowerPoint	0.499	Orange	Hootsuite	0.395	Red
21	Twitter	0.496	Purple	Social media marketing	0.382	Red
22	Email campaigns	0.483	Red	Marketing automation	0.377	Red
23	Social media channels	0.479	Purple	Microsoft Office	0.369	Orange
24	HTML	0.475	LBlue	Marketing campaigns	0.360	Blue
25	Microsoft Office	0.470	Orange	Word	0.344	Orange
26	Content management	0.465	Red	Content marketing	0.330	Red
27	Marketing campaigns	0.455	Red	Pinterest	0.319	Green
28	Hootsuite	0.444	Blue	Content management	0.311	Red
29	Instagram	0.439	Purple	Snapchat	0.297	Green
30	Campaign management	0.426	Red	Presentation	0.272	Blue
31	Marketing strategies	0.418	Red	PowerPoint	0.266	Orange
32	Photoshop	0.417	LGreen	Photoshop	0.264	LBlue
33	Content strategy	0.416	Red	Tableau	0.264	Red
34	Social media campaigns	0.397	Red	Collaboration	0.261	Blue
35	Programming	0.395	Blue	Content strategy	0.258	Red
36	Social media marketing	0.375	Red	Social media channels	0.257	Green
37	Word	0.373	Orange	Web development	0.249	Blue
38	Web development	0.373	LBlue	SQL	0.248	Purple
39	Organizational	0.371	Green	YouTube Ads	0.247	Red
40	Presentation	0.358	Green	HTML	0.245	Purple
41	Pinterest	0.354	Purple	Advertising campaigns	0.234	Red
42	SQL	0.348	Blue	Team player	0.228	Blue
43	Adobe Analytics	0.346	Blue	Adobe Analytics	0.225	Red
44	Advertising campaigns	0.344	Red	InDesign	0.222	LBlue
45	Tableau	0.339	Blue	Online marketing	0.222	Red
46	Facebook Ads	0.330	Blue	Organizational	0.217	Blue
47	Analytics	0.321	Green	Instagram Ads	0.211	Red
48	Marketing experience	0.311	Red	Email campaigns	0.200	Blue
49	Media buying	0.296	Red	Campaign management	0.192	Red
50	Strategy development	0.294	Red	Marketing communication	0.182	Blue
51	Data management	0.292	Red	Media buying	0.182	Green
52	InDesign	0.285	LGreen	Marketing management	0.181	Blue
53	Social media strategies	0.284	Red	Illustrator	0.180	LBlue
54	Paid social media	0.280	Red	Social media content	0.178	Blue
55	Social media accounts	0.279	Purple	Data science	0.176	Blue
56	Marketing communication	0.277	Red	Interpersonal	0.173	Blue
57	Team player	0.272	Red	Social media accounts	0.168	Green
58	Business development	0.271	Red	Paid social media	0.168	Red
59	Adobe Creative	0.262	Orange	Data analytic	0.168	Blue
60	Brand management	0.260	Red	Twitter Ads	0.168	Red
61	Digital content	0.259	Green	Email marketing campaigns	0.164	Red
62	CSS	0.257	LBlue	Time management	0.155	Blue
63	Customer experience	0.256	Red	Analytics	0.154	Blue
64	Marketing plans	0.256	Red	Basic html	0.154	Purple
65	Verbal	0.255	Green	Business development	0.153	Blue
66	Web content	0.253	Red	Digital advertising	0.152	Red
67	Business intelligence	0.249	Red	Adobe Creative	0.148	Orange
68	Online marketing	0.241	Red	Programming	0.145	Purple
69	Basic html	0.238	LBlue	Social media presence	0.143	Green
70	User experience	0.238	Red	CSS	0.137	Purple
71	Marketing management	0.235	Red	Google Tag	0.134	Red
72	Brand awareness	0.234	Red	Sprinklr	0.132	Red
73	Email marketing campaigns	0.231	Red	Verbal	0.131	Blue
74	Digital channels	0.229	Red	Data visualization	0.130	Red
75	Social media content	0.228	Red	Oral	0.130	Blue
76	Data visualization	0.228	Blue	User experience	0.128	Blue
77	Marketing channels	0.221	LBlue	Social media posts	0.120	Blue
78	Social media analytics	0.220	Red	Data driven	0.117	Blue
79	Data driven	0.214	Red	Digital marketing campaigns	0.111	Red
80	Oral	0.212	Green	Python	0.111	Purple
81	Digital marketing experience	0.211	Red	Marketing channels	0.110	Blue
82	Google Tag	0.206	Blue	Web content	0.110	Blue
83	Illustrator	0.205	LGreen	Display network	0.110	Red
84	Digital marketing campaigns	0.202	Red	Digital content	0.110	Blue
85	Time management	0.202	Green	Social media analytics	0.109	Red
86	Interpersonal	0.200	Green	Social media campaigns	0.104	Green
87	Data science	0.200	Red	Strategy development	0.104	Blue
88	SAS	0.197	Blue	Kenshoo	0.100	Red
89	Python	0.197	Blue	Campaign development	0.098	Red
90	Snapchat	0.197	Purple	Digital channels	0.097	Blue
91	Campaign development	0.194	Red	Integrated marketing	0.092	Blue
92	Social media posts	0.190	Green	Nielsen	0.091	Red
93	Management experience	0.184	Blue	Customer experience	0.091	Blue
94	Social listening	0.179	Red	Facebook Insights	0.090	Red
95	Sprinklr	0.176	Blue	Brand management	0.085	Blue
96	Facebook Insights	0.172	Blue	Social listening	0.085	Red
97	ComScore	0.170	Blue	Business intelligence	0.080	Blue
98	Data mining	0.162	Blue	Facebook Page	0.077	Green
99	Display Network	0.161	Blue	Microsoft Suite	0.075	Orange
100	Data analytic	0.148	Red	marketing plans	0.071	Blue

Note. Red is the color of the largest cluster. Blue is the color of the second-largest cluster. Green is the color of the third largest cluster. Purple is the color of the fourth largest cluster. Orange is the color of the fifth largest cluster. LBlue represents Light Blue that is the color of the sixth largest cluster. LGreen represents Light Green that is the color of smallest cluster.

For the remote positions, the largest clusters took 36.44% of the network and centered around Google Analytics. Different from the in-person job descriptions, this cluster of tools about digital advertising was also strongly associated with skills about marketing automation, email marketing, social media marketing, social media management, and social media analytics. The second largest cluster (34.75%) centered around the skill of research which was closely tied to the skills of marketing strategies, web development, as well as writing. Different from the in-person network, there was not any salient cluster with the skill of communication as a hub. The skill of communication co-occurred most frequently with writing, a salient node in the research cluster, and was strongly associated with the skill of content creation. Similar to the in-person network, Facebook was also the hub of the third largest cluster (11.86%) and closely tied to a set of social media tools and skills. The fourth largest cluster (8.47%) centered around the tool of SQL, clustering with the tools of HTML, CSS, SAS, SPSS, Python, and programming skill. Compared with the in-person network, the remote network also had a relatively smaller Microsoft Office cluster (5.08%) and a larger Adobe Creative Suite cluster (3.39%).

Discussion

Through text mining and semantic network analysis, this study compared the in-person and remote jobs requiring media analytics to inform educators about integrating analytics in the media and communications curriculum. The findings indicate that both the in-person and remote positions emphasized the competencies of conducting research, developing strategies, communication, and collaborations as well as the mastery of the tools about data analysis, digital marketing, and content creation. These expectations are consistent with the CPRE’s (2018) and ACEJMC’s (2012) emphasis on integrating research, analysis, and data into media and communications curriculum. However, these expectations also suggest that knowledge and skills about data and analytics are rarely sought alone. Instead, employers expect the candidates to apply them in conjunction with other media and communication domain-specific knowledge to drive strategy development and decision-making, which is rooted in the original definition of analytics (Dinsmore, 2016).

Similar to the earlier research by Stansberry and MacKenzie (2020), communication emerged as a top skill desired by both types of positions. However, the nature of the working mode influenced which aspect of communication they emphasized on (Coate, 2021). Specifically, the in-person positions emphasized more on verbal, interpersonal, and organizational communication, whereas the remote positions asked more for written communication. In addition, while the in-person positions had higher expectations on the general data management and analysis tools, the remote positions underlined the use of analysis and advertising tools related to specific social media platforms and search engines.

Informed by these findings, the programs in media and communications should consider incorporating the following topics into the curriculum to prepare students for positions requiring media analytics. First, considering the high mention frequency of Excel in the descriptions of in-person positions, the use of spreadsheets should be integrated into the existing curriculum to lay the foundation for managing large datasets and creating effective visualizations for reporting. For example, Excel labs can be integrated into writing classes to introduce how to match various types of audience datasets with appropriate visualizations for creating business and research reports. As called by ACEJMC (2012), students need to be equipped with the knowledge of basic statistics and metrics for describing the audience’s interaction with traditional and emerging media. More importantly, the most salient skills that distinguish communication students from computer and data science students are storytelling, presentation, and collaboration. Group work should be provided to allow students to collaboratively analyze the user data. Plenty of oral presentation opportunities are needed for students to practice using data visualizations to communicate insights and better manage brands.

The COVID-19 pandemic not only increased the share of remote internships as reported by Indeed Hiring Lab (Konkel, 2021) but also led to more remote internship opportunities in digital and social media management and marketing, which are highly relevant to college students majoring in media and communications. To get them prepared with essential analytical skills for this kind of internship, we need to teach analytics beyond the spreadsheets and integrate more hands-on activities about social listening using both popular free platforms, such as Facebook Ads & Insights, Instagram Ads & Insights, Twitter Ads & Analytics, and YouTube Ads & Insights, as well as paid analytics platforms, such as Brandwatch. It is even more helpful to dedicate some courses to social listening and social media content creation. Furthermore, students need to be equipped with the ability to use Google Analytics to track and understand media users’ digital footprints on a variety of social media platforms, employing data to gauge audience engagement (e.g., acquisition and conversion), and developing/optimizing social media marketing and campaign strategies. For example, students could work on a project to first conduct market research through social listening, develop a social media marketing plan and/or campaign through content creation, and then use Google Tags and Google Analytics to track and assess the effectiveness of the plan.

Aside from informing curriculum development, the findings of this study also shed light on some actions to be taken by student professional development centers (SPDCs) to better prepare students for careers requiring media analytics. For example, SPDCs could use the keywords and key phrases of skills and tools identified in this study (listed in Tables 4 and 5) to help students better understand the employers’ expectations. By using the target keywords and key phrases recognized by employers, students will be able to build effective resumes and develop coherent personal narratives to demonstrate their knowledge and skills about media analytics in interviews. Media analytics is a fast-growing field. Expanding students’ connection with employers will allow them to obtain update-to-date know-hows from the field.

There are several limitations to this study. First, although text mining and semantic network analysis allow us to examine a large number of job ads, it is limited to quantitative analysis of keywords and key phrases. Future research could integrate qualitative analysis to gain a more nuanced understanding of the industry expectations. Second, the data were collected during the first and second waves of the COVID-19 pandemic in the United States when most companies were functioning in the remote mode. Future research could expand the data collection period to compare the in-person and remote positions at different time points, such as before, during, and post the pandemic, or at different waves. This comparative research could further enrich the findings discovered in this study.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Ke Jiang (Ph.D.) is an assistant professor of media analytics in the School of Communications at Elon University. She is a computational social scientist in the areas of network analysis, natural language processing, and social media analytics. Her research interests focus on the media effects on the macro-society level, as well as the evolution of digital culture and humanity.

Qian Xu (Ph.D.) is a professor of strategic communications in the School of Communications at Elon University. She teaches courses on media analytics and strategies for interactive media and social media. Her research interests focus on the social and psychological effects of media technology, as well as the pedagogy of high impact practices.

Ashleigh Afromsky is a recent graduate from Elon University with bachelor degrees in Media Analytics and Communication Design.

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