LinkedIn campaign manager

Before launching any advertisements, several prerequisites must be met. Researchers require:

1. a personal LinkedIn account,
2. a LinkedIn ad account, and
3. a LinkedIn page.

Each component serves a distinct purpose in the LinkedIn advertising ecosystem. The creation of all three components is free of charge.

LinkedIn ad account

Once a personal profile exists, an advertising account can be established via the LinkedIn Advertise platform. Selecting “Create ad” or “Get Started” initiates the process (see Figure 1).

Figure 1: Starting page of LinkedIn Advertise website.

_{^{Source: LinkedIn Advertise}}

The platform then prompts the user to assign an account name and link the account to a LinkedIn Page (see Figure 2). If no appropriate institutional page is available, a new one must be created.

Figure 2: LinkedIn ads account set up.

_{^{Source: LinkedIn Campaign Manager}}

After this step, the ad account provides access to campaign creation, audience definition, and budgeting functions.

LinkedIn page

A LinkedIn page functions as the institutional or organizational identity under which advertisements are displayed. Depending on the project’s context, the page may represent:

• a company or organization (e.g., a research institute),
• a showcase page (a subpage linked to a main institutional account), or
• an educational institution (e.g., an University).

Unlike platforms such as Facebook (Meta), LinkedIn does not currently offer a dedicated project or educational page type, meaning researchers must register under one of the existing categories. Each page requires basic information such as a name, a public URL, the industry, and the size of the organization (see Figure 3). In addition, it is possible to indicate further information (i.e., logo and a short organizational slogan). For research recruitment, using an institutional page (for example, a university department or research group) can increase credibility and transparency in the eyes of potential participants.

Figure 3: Set up LinkedIn page – example: educational institute.

_{^{Source: LinkedIn Campaign Manager}}

Setting up an ad campaign

The LinkedIn advertising system follows a three-tier hierarchical structure:

Figure 4: LinkedIn advertising system

Each tier governs a different aspect of the set-up, from defining overall objectives to specifying detailed audience characteristics and ad creatives. Understanding this structure is essential for organizing an effective and transparent recruitment effort.

Campaign level

At the highest level, the campaign establishes the overarching goal and organizational framework of the recruitment project. LinkedIn offers three types of objectives (see Figure 5):

• Awareness (increasing visibility),
• Consideration (encouraging engagement), and
• Conversion (prompting specific actions).

For survey recruitment, the “Website visits” option under the Consideration category is generally most suitable, as it directs users to an external landing page, typically the survey entry point.

Figure 5: Choosing a campaign objective

_{^{Source: LinkedIn Campaign Manager}}

Once the campaign objective has been chosen, LinkedIn prompts the user to select a campaign type. Two options are available: a manual setup (“Classic”) and an AI-assisted setup (“Accelerate”) (see Figure 6). While the latter automatically adjusts parameters to optimize engagement, we strongly recommend avoiding it. Algorithmic optimization introduces unknown and uncontrolled sources of bias into audience selection, thereby comprising the transparency and replicability required in scientific research. Manual configuration ensures that all targeting and budgeting decisions remain under the advertiser’s, that is, the researcher’s control and can be documented in detail.

Figure 6: Selecting a campaign type

_{^{Source: LinkedIn Campaign Manager}}

After selecting the campaign type, researchers must determine the ad creation process, that is, how individual advertisement will be assembled and optimized within the campaign. Again, LinkedIn offers two routes: Flexible ad creation and standard ad creation (see Figure 7). In the flexible ad creation mode, the platform automatically generates and tests different combinations of ad elements (e.g., images, headlines, and descriptions) and allocates the budget to those combinations that achieve the best predicted performance. This process resembles A/B testing but is fully automated. Although potentially efficient, this approach lacks transparency. The platform does not disclose the criteria used to define “best performance” nor how these predictions are generated. Consequently, it introduces algorithmic bias and variation that are not under research control. In research contexts, the standard ad creation mode is clearly preferable. This mode requires researchers to manually configure each ad creatively. All text and visual elements, as well as audience and delivery settings, are explicitly defined by the researchers. This manual process ensures that recruitment materials remain consistent and that all campaign parameters are fully reproducible and can be reported in a methods section.

Figure 7: Selecting an ad creation process

_{^{Source: LinkedIn Campaign Manager}}

After determining the creation route, the campaign configuration also includes the definition of schedule and budget parameters (see Figure 8). Researchers may specify:

• a start and end date for the campaign,
• a total (lifetime) budget, and
• whether budget optimization across campaigns should be enabled.

Again, we recommend disabling all forms of automated budget optimization. Although these features can improve marketing efficiency by reallocating funds to higher-performing ads, they rely on algorithmic assessments that are not accessible to researchers and could bias the recruited sample in unknown and unobservable ways.

Figure 8: Campaign details

_{^{Source: LinkedIn Campaign Manager}}

Ad set level

At ad set level, the researcher defines the target population, that is, the group of LinkedIn users who will be exposed to the survey advertisement. LinkedIn provides numerous targeting attributes derived from the members’ professional profiles, company pages, and inferred behavioral or interest signals (see Figure 9). Table 1 provides a comprehensive overview of the main attribute categories and typical options within each. Where known, information on how LinkedIn obtains or infers that information is indicated.

Table 1: LinkedIn Attributes and Data Sources

_{^{Note: Data availability and functionality may vary by region due to privacy regulations, particularly with the Europeam Economic Area and Switzerland.}}

Figure 9: Definition of target audience

_{^{Source: LinkedIn Campaign Manager}}

As seen in Table 1, researchers have various options for target parameters. Therefore, is important that targeting parameters must never be used to discriminate against individuals based on protected characteristics such as gender, age, or actual or perceived race or ethnicity. Researchers should also comply with platform-specific restrictions and regional regulations that limit the use of sensitive targeting criteria. The option “enable audience expansion” should be deactivated (see Figure 9). This algorithmic extension may deliver ads to users outside the intended target population and thus undermine the transparency and controllability of the sample frame. LinkedIn’s Campaign Manager also displays an estimated performance forecast, showing projected target audience size, impressions, clicks, and costs (see Figure 10). These values can assist in budget planning but are based on proprietary models and should be interpreted as approximate indicators only, not as precise predictions.

Figure 10: Forecasted results

_{^{Source: LinkedIn Campaign Manager}}

Once the campaign objectives and audience have been defined, the next step is the selection of an appropriate advertisement format (see Figure 11). LinkedIn currently offers several formats that differ in visual layout, degree of interactivity, and expected engagement levels. The choice of format should align with the overall research design, recruitment objectives, and desired intensity of participation involvement. Available formats include, among other:

• Single image ads: static image with headline and text,
• Carousel ads: multiple scrollable image cards that allow the presentation of different aspects or topics within one advertisement, and
• Video ads: short clip (15-30 seconds) combining visuals and text to communicate information dynamically.

For most survey recruitment, these formats are generally the most suitable due to their high visibility and clear message presentation.

Figure 11: Ad format

_{^{Source: LinkedIn Campaign Manager}}

The integration of URL tracking parameters (e.g., UTM codes) into the destination link is recommended to enable transparent performance monitoring (see Figure 12). A typical example would be:

utm_source=[SOCIAL MEDIA PLATFORM]&utm_medium=[PAYMENT EVENT]
&utm_campaign=[CAMPAIGN NAME]&utm_content=[AD ID].

This method complies with data protection requirements and does not collect personal information. It simply allows researchers to distinguish traffic from different platforms or creatives in their web analytics.

Figure 12: URL tracking parameters

_{^{Source: LinkedIn Campaign Manager}}

Ad placement is then specified (see Figure 13). The LinkedIn feed should be selected as the primary placement to ensure visibility in a professional context. The LinkedIn Audience Network, which extends ad delivery to partner websites and apps, should be deactivated in research campaigns to maintain control and transparency regarding where exactly recruitment occurs.

Figure 13: Placements option

_{^{Source: LinkedIn Campaign Manager}}

The budgeting model determines how funds are distributed over time (Figure 14). LinkedIn provides three options:

• Daily budget: specifies maximum daily expenditure, ensuring consistent ad delivery,
• Lifetime budget: sets a total expenditure limit for the campaign period, and
• Combined budget: applies both daily and lifetime limits for maximum control.

Short-term campaigns benefit from daily budgets to maintain stable exposure, while longer recruitment periods may require lifetime or combined budget settings. Key performance metrics such as impressions, click-through rate (CTR), and cost per click (CPC) should be monitored throughout the campaign and reported alongside sample characteristics.

Figure 14: Budget and schedule options

_{^{Source: LinkedIn Campaign Manager}}

The bidding strategy determines how LinkedIn allocates ads in its auctions system (Figure 15). LinkedIn currently offers three bid types:

• Maximum delivery: an automated bidding option where LinkedIn’s system sets the bid with the goal of spending the full budget and maximizing the selected key result,
• Cost cap: an automated option where LinkedIn sets bids while trying to keep the average cost per key result close to a user-specified target (e.g., CPC or CPM), and
• Manual bidding: a more hands-on option where advertisers set an explicit bid value that is used in the ad auction.

From a research perspective, there is a trade-off between cost-efficiency and methodological control. Fully automated options such as maximum delivery can be convenient and may help use the budget efficiently, but they also shift additional decision-making to opaque algorithms. Where fine-grained cost control and transparency are priorities, cost cap or even manual bidding may be preferable, provided that campaigns are monitored closely, and bid levels are adjusted if necessary. Regardless of the chosen strategy, researchers should document the bid type and any changes during fieldwork so that recruitment conditions can be accurately reported.

Figure 15: Bidding options

_{^{Source: LinkedIn Campaign Manager}}

Although LinkedIn supports conversion tracking via tracking pixels (see Figure 16), this method is ethically and legally problematic in research contexts. Tracking pixels typically collect behavioral data across websites (e.g., page views, time spent, subsequent actions) and can be used to link on-platform interactions with off-platform behavior. Under data protection frameworks such as the General Data Protection Regulation (GDPR), such tracking requires a valid legal basis and, in most cases, informed consent: participants must be clearly informed before any tracking takes place about what data are collected, for what purpose, on which legal basis, how long they are stored, and with whom they may be shared. Only if users have actively agreed (e.g., via a consent banner or consent form on the landing page) may tracking pixels be activated. However, for scientific campaigns, this form of tracking is usually difficult to justify. Social media platforms and advertising networks have repeatedly been affected by data breaches and other security incidents (e.g., Das 2023; Feathers et al. 2022; Fondrie-Teitler et al. 2022). When third-party tracking is enabled, additional actors gain access to behavioral data and identifiers, increasing the potential attack surface. Even if the probability of a breach is difficult to quantify, the possible consequences for participants – for example, the unintended disclosure of sensitive behavioral patterns or re-identification risks when log data are leaked – can be substantial. From a risk-minimization perspective, avoiding non-essential tracking is therefore a central protective measure. We therefore recommend that researchers do not use LinkedIn’s conversion tracking or similar third-party tracking pixels for recruitment. Instead, evaluation should rely on aggregate campaign statistics (impressions, clicks, CPC) provided by the platform, combined with survey-based indicators (e.g., completion rates and a self-reported recruitment channel question on the first page of the questionnaire, asking participants how they became aware of the study).

Figure 16: Conversion tracking

_{^{Source: LinkedIn Campaign Manager}}

Ad level

At the ad level, the creative elements that LinkedIn users are exposed to are defined. The design of these elements can draw on established findings from research on invitation letters in survey methodology. In line with this literature, ad content should be convincing and informative, and should convey a clear perceived leverage for participation, for example by highlighting the social value of the study, the relevance of the topic for the target population, or the opportunity to contribute one’s perspective (e.g., Dillman et al. 2014 ; Groves et al. 2000). On LinkedIn, as on other social media platforms, an ad competes with a large volume of other digital content. Consequently, the ad must first attract attention within a very short time frame and then provide sufficient relevance and clarity to motivate users to click on the ad and proceed to the survey landing page. The introductory text (Figure 17) – typically the most prominent text element in the ad – should succinctly communicate the purpose of the study and its relevance for the targeted audience. A concise structure is advisable: one sentence specifying what the study is about and who it addresses, followed by a sentence that conveys why participation is meaningful (e.g., expected benefits, contribution to knowledge, or policy relevance).

Figure 17: Ad design - Text and media input

_{^{Source: LinkedIn Campaign Manager}}

The visual content serves as the primary attention grabber and can function as an additional targeting device (Figure 17). On LinkedIn, visuals are often embedded in a professional and organizational context, for example by depicting typical work situations (e.g., team meetings, hybrid collaboration, industry-specific workplaces), abstract graphics related to the topic (e.g., charts, icons for digitalization or skills), or neutral employer-branding style imagery. When these visuals are clearly aligned with a specific survey topic – for instance, leadership and management practices, remote work arrangements, skills development, or employee well-being – they can increase click-through rates and the number of completed questionnaires, because they immediately signal topical relevance to certain occupational groups or sectors. At the same time, these visuals may amplify self-selection on work-related attitudes or experiences (e.g., job satisfaction, perceived stress, views on organizational change), as they are particularly salient for individuals with above-average involvement or strong opinions (Donzowa et al. 2025; Zindel et al., 2025). This implies a trade-off: if recruitment within a narrowly defined topic area is prioritized, topic-specific professional visuals are advantageous; if concerns about attitudinal bias are central, more neutral or general workplace imagery may be preferable. In addition to the visual itself, LinkedIn offers the option to set an image alt text (Figure 18). This alt text enhances accessibility for users who rely on screen readers and is displayed if the image cannot be rendered. From a survey recruitment perspective, the alt text should provide a concise, neutral description of what is shown in the image and, where appropriate, can briefly restate the topic of the study (e.g., “Illustration of professionals discussing climate policies in an office setting”). Overly promotional language or keyword stuffing should be avoided. The goal is to ensure that the meaning of the visual remains understandable even in the absence of the actual image and that the alt text is consistent with the overall framing of the study (cf. [World Wide Web Consortium [W3C], 2025[(https://www.w3.org/TR/WCAG21/)]).

Figure 18: Ad design - Further options

_{^{Source: LinkedIn Campaign Manager}}

The destination URL is the link to the survey website and leads directly to the survey entry page, without intermediary pages or redirects. This entry page must present the informed consent statement and essential information on data protection, study sponsors, and contact details before data collection begins. From a user-experience perspective, the number of steps between the ad (i.e., the survey invitation) and the first survey question should be minimized, as each additional step potentially increases drop-off probabilities (cf. Tourangeau et al. 2013). Besides the introductory text and the image, LinkedIn ads typically include a headline and an optional ad description. The headline is usually displayed in a highly prominent position and should therefore be short, specific, and benefit oriented. For survey recruitment, headlines that clearly state the topic and target group (e.g., “Short survey on remote work for HR professionals”) are generally preferable to vague or purely promotional formulations. The ad description provides additional information to people who see the ad. However, this field is not visible in most scenarios and will only appear for a small portion of LinkedIn members, for example in certain placements on the LinkedIn Audience Network (if enabled). Accordingly, essential information about the study should not be placed exclusively in the description but should already be contained in the introductory text and headline. The description can be used as a secondary field to elaborate on the headline, clarify what happens after the click, indicate approximate survey length, or highlight a specific leverage (e.g., “10-minute academic survey, anonymous, results will inform future workplace policies”). Given that descriptions may be truncated or omitted depending on device and placement, key information should appear at the beginning of the text and should not be critical for understanding the study. The call-to-action (CTA) button should be consistent with the overall framing of the ad and align with user expectations on a professional platform. LinkedIn offers several standard CTA options, including “Learn more”, “Sign up”, “Register”, and others. For survey recruitment, a CTA such as “Learn more” is typically most appropriate, as it signals low-threshold, information-oriented action compatible with participation in a research study. In settings where the survey is embedded in a broader program or panel, “Sign up” or “Register” may also be suitable.

Figure 19: Ad preview

_{^{Source: LinkedIn Campaign Manager. Image by Africa Studio via stock.adobe.com.}}

Prior to launch, the preview function in Campaign Manager should be used to verify appearance across devices and placements (see Figure 19). Across all ad components, internal consistency is crucial. The introductory text, headline, description, visual elements (including alt text), destination URL, and CTA should convey a coherent message and set realistic expectations about the survey experience. A consistent, methodologically informed ad design that is at the same time adapted to platform-specific norms on LinkedIn potentially increases the likelihood of attracting suitable respondents rather than merely generating clicks.

Ad campaign details

Following the three-tier campaign structure described above, the campaign was configured at the campaign, ad set, and ad levels. A dedicated LinkedIn page titled “Empirische Sozialforschung – Uni Bielefeld” (English translation: “Empirical Social Research – Bielefeld University” was created to host the ads and serve as the institutional identity for the recruitment process (see Figure 20).

Figure 20: LinkedIn page: Empirical social research – Bielefeld University

Ad creatives

Two separate advertisements in the single image format were developed. They used identical textual content but different ad images to examine how visuals influence engagement (Figures 21 and 22).

Headline: We want your opinion: Take part in our survey on the German labor market! Introductory text: Help us better understand the German labor market! We are looking for participants for a short survey that is part of a scientific study conducted by Bielefeld University. The survey is aimed at all employed people aged 18 and over in Germany.

Figure 21: Targeting ad 1 used to recruit participants on LinkedIn

_{^{Source: LinkedIn Campaign Manager. Image by mitay20 via stock.adobe.com.}}

Figure 22: Targeting ad 2 used to recruit participants on LinkedIn

_{^{Source: LinkedIn Campaign Manager. Image by Africa Studio via stock.adobe.com.}}

Campaign results

The LinkedIn recruitment campaign ran for five consecutive days, from 6 to 10 July 2024, and was managed through LinkedIn Campaign Manager using the institutional page created for this project. Across both advertisements, the campaign reached a substantial professional audience and generated a sizeable number of completed interviews within a modest budget. Table 2 summarizes the performance metrics for both advertisements. In total, the two ads produced 56,990 impressions, resulting in 301 link clicks. Of those who clicked on the advertisement, 143 started the interview and 98 completed the questionnaire. With a total spend of 500 EUR, this corresponds to an overall cost per click (CPC) of 1.66 EUR and a cost per completed interview (CI) of 5.10 EUR. These results suggest that LinkedIn Ads can serve as an efficient recruitment channel for workplace-related surveys, producing a relatively high number of completed questionnaires at moderate cost.

Both ads achieved a click-through rate (CTR) of roughly 0.5% (0.54% for Ad 1 and 0.52% for Ad 2). The overall CPC was 1.66 EUR, with a slightly lower CPC for Ad 1 (1.58 EUR) than for Ad 2 (1.73 EUR). At the same time, Ad 2 produced more completed interviews (57 compared to 41) and therefore a slightly lower cost per completed interview (5.03 EUR for Ad 2 versus 5.20 EUR for Ad 1). These differences are numerically small, but they show that the two creatives did not behave identically in terms of cost structure, even though they were identical in text and targeting. The delivery patterns of the two ads are also differed. Ad 1 was shown to a larger number of unique users (reach 7,269) and with a lower average frequency (3.45 displays per user). Ad 2, by contrast, reached fewer unique users (5,189) but was displayed more often to those who were reached (average frequency 6.15). Thus, the total number of impressions was higher for Ad 2, even though its reach was lower. Within the constraints of the shared targeting, this indicates that LinkedIn’s delivery system concentrated Ad 2 more strongly on a subset of the potential audience, while Ad 1 was distributed more broadly.

Sample composition

The composition of the achieved sample reflects the characteristics of those LinkedIn users who clicked on the advertisements, proceeded to the survey, and completed the questionnaire. Table 3 shows the distribution of the sociodemographic variables. Respondents are distributed across all age groups included in the questionnaire, with the largest shares in the ranges 45–54 years (31.6%) and 35–44 years (29.6%) (age mean: 45.84; Range: 23-69). Regarding gender, 62 respondents identify as female and 32 as male, while four respondents did not answer this question. No respondent selected the response option for another gender identity. Education attainment in the sample is strongly concentrated at the higher end of the scale: Most respondents have a higher secondary school diploma (85.7 %), further 10.2 percent hold a secondary school diploma. If we compare the composition of sociodemographic variables with the whole population in Germany, we see that the sample contain relatively few young people (Statistisches Bundesamt 2025a). Even if 21.4 percent of the sample are 55 years and older, only 10.4 percent are 60 years and older, and no respondents are older than 70. In 2023, 29,8 percent of the population were 60 years and older based on Zensus (Statistisches Bundesamt 2025a). These differences may be due to the fact that LinkedIn is used particularly by people of working age who are neither in education nor retired. Moreover, the percentage of women and the educational attainment in the sample is higher than in the whole population in Germany (Statistische Ämter des Bundes und der Länder 2025a; Statistische Ämter des Bundes und der Länder 2025b).

Table 4 presents the occupational variables. Most respondents report full-time employment (75.5%), while 19.4 percent work part-time. Only four respondents indicated marginal or no employment. In 2024 in Germany, the unemployment rate was 3.1 percent, and the part-time rate was 29,1 percent (Statistisches Bundesamt 2025b; Statistisches Bundesamt 2025c). Firm size is also skewed toward larger organizations: 58.2 percent of the respondents work in companies with more than 200 employees, and 24.5 percent work in firms with between 20 and 200 employees. Only 15.3 percent of respondents report working in small firms with fewer than 20 employees. Based on Destatis, in 2023, nearly the half of the German population work in large companies (Statistisches Bundesamt 2025d). With regard to leadership roles, 24.5 percent indicate that they hold a leadership position, while 59.2 percent do not. Notably, 16 respondents did not answer this question, resulting in a higher share of missing values than the other occupational variables. The distribution across industry sectors covers a broad range of fields. The largest groups of respondents work in manufacturing (15.3%), information and communication (15.3%), and human health and social work activities (15.3%). Other sectors represented in the sample include scientific and technical activities, education, financial and insurance activities, public administration, and several smaller categories. Even if the share of people working in the manufacturing sector is smaller in the sample than in the general population, that a relatively high percentage work in the area of services (e.g., education or human health and social work activities) is also true in the general population (Statistisches Bundesamt 2025e; Statistisches Bundesamt 2025f).

The discussed patterns are consistent with previous evidence that LinkedIn use is concentrated among higher-income professionals and knowledge-intensive occupations (Van Dijck, 2013; Blank & Lutz, 2017) and underlines that LinkedIn-based samples capture a selective segment of the working-age population.

The TikTok Ads Manager

Having an active TikTok account is the first step towards advertising and recruiting survey participants on TikTok. This is because only individuals and organizations with registered business accounts are allowed to run ads on the platform. To register for a business account, go to the TikTok for Business website and proceed by clicking the “Create Now” button (see Figure 1).

Figure 1: Starting page of TikTok for Business website.

From there, follow the prompts to sign up for a new account. You will need to provide basic information such as your email address and a password to create an account. Once you have an account, you can proceed to set up your business profile (see Figure 2). This includes providing information such as your company name, industry and country. Make sure to accurately and clearly represent your business, as this will help to build trust with potential survey participants.

Figure 2: TikTok for Business profile.

Setting up an ad campaign

When creating an advertising campaign on TikTok, it is important to familiarize yourself with the campaign structure. The campaign settings are structured into three distinctive hierarchical levels: campaign level, ad group level, and ad level (see Figure 3). Within the ad campaign, you create ad groups, which serve as subdivisions allowing for further targeting refinement. Each ad group can contain multiple individual ads that serve as the creative content displayed to TikTok users. This means that within a single ad campaign, you can create multiple ad groups, and within each ad group, there can be multiple ads. Each level requires making informed choices and providing necessary information to ensure the effectiveness of your ad campaign.

Figure 3: TikTok ad hierarchy.

Campaign level

At the campaign level, the first step is to choose the campaign objective that best aligns with your advertising goal. This decision will determine how TikTok will optimize the ad delivery. TikTok offers three main categories of campaign objectives: awareness, consideration, and conversion (see Figure 4). Each category serves a distinct purpose in terms of user interaction with your ad and the desired actions you want users to take.

Figure 4: Selecting an advertising objective.

_{^{Source: Tik Tok Ads Manager (after login)}}

Firstly, awareness campaigns are aimed at sparking interest and enhancing the visibility of a brand, product, or service. These campaigns are designed to reach as many people as possible and create a lasting impression of a brand. Secondly, consideration campaigns are focused on driving engagement and interest. This includes encouraging users to visit a website, interact with content, or follow an account. These campaigns are designed to provide users with more information about a brand, product, or service, and to motivate them to take a specific desired action. Lastly, conversion campaigns are designed to drive users towards a specific action, such as making a purchase or downloading an app.

When creating a TikTok ad campaign to recruit survey participants, we highly recommend setting the campaign objective to “Traffic”, which is categorized under consideration. This objective focuses on driving traffic to a designated landing page, such as your survey website. By selecting this objective, your ad campaign will be optimized to effectively direct users to your survey website, increasing the likelihood of attracting potential participants to take part in your survey. TikTok’s machine learning algorithms will be leveraged to identify users within the target audience who are most likely to take the desired action of clicking on the ad and visiting your landing page.

After selecting the campaign objective, you can proceed to define some essential settings of your campaign. These settings include providing a campaign name and defining a maximum budget for the entire campaign (see Figure 5). While the maximum budget is not mandatory, we recommend setting it as a precautionary measure to prevent your campaign from exceeding the spending limit of your research project.

Figure 5: Defining campaign name and campaign budget.

_{^{Source: Tik Tok Ads Manager (after login)}}

Ad group level

At this level, you can create multiple ad groups within a campaign, each with its own settings tailored to the target audience, thereby enhancing ad effectiveness. You can specify various settings unique to each ad group. These include the optimization location, placements, targeting, budget and schedule, bidding, and optimization. First, set the optimization location, i.e. where to direct traffic. For survey recruitment, the optimization location should be set to the survey website where participants can complete the survey. Under the traffic objective, ad placement in Germany is limited to TikTok and Pangle (see Figure 6). While TikTok is the primary platform where ads will appear, Pangle is an ad network that provides access to third-party apps. We recommend to choose TikTok instead of Pangle, as TikTok has a larger user base and provides more advanced targeting options, which can help you reach a more specific audience. For other countries, other ad placements are available (for an overview see TikTok (n.d.)). Additionally, within the placements section, you have the option to customize your ad settings to control the level of user engagement. Consider deactivating comments to avoid harmful or violent reactions, disabling video downloads to protect your content, and restricting video sharing to ensure that your ads are exclusively viewed within the TikTok platform.

Figure 6: Defining placement options.

Targeting is another crucial step at the ad group level where you define the audience that your ad will be shown to. TikTok provides a range of targeting options, including demographic information, interests, behaviors, and custom audiences. You should define the target audience based on your research objectives and select the appropriate targeting options to effectively reach your desired audience. Take advantage of the wide range of targeting options available when running TikTok ads. Here are some examples:

• Demographic targeting: Gender (binary), age groups, and language preferences (see Figure 7).
• Geographic targeting: Countries/regions you can target using “Location” depending on the country/region associated with your TikTok For Business account. For a comprehensive list of all available locations per country, see TikTok (n.d.).
• Interest-based targeting: Reach users based on their inferred interests, derived from their activity on TikTok (see Figure 8).
• Behavioral targeting: Reach users based on their behavior on TikTok, such as the videos they have liked, shared, or commented on (see Figure 8).

Figure 7: Defining demographic targeting parameters.

Figure 8: Defining interest- and behavior-based targeting.

It is crucial to ensure compliance with TikTok’s advertising policies and avoid any form of discriminatory targeting towards protected groups. TikTok maintains strict advertising policies to uphold community standards and legal requirements. Advertisers are expected to follow these policies, which are regularly updated, in order to maintain the quality and integrity of the TikTok platform. For a detailed description of all prohibited and restricted content, please refer to TikTok (n.d.).

The next step at the ad group level is to set a budget and schedule. You should define a daily budget for each ad group and specify the display schedule for each group. TikTok offers an estimation of an ad group’s potential reach based on the selected targeting options. This estimation can assist in determining an appropriate budget for your campaign (see Figure 9).

Figure 9: Estimated audience reach.

Once you have set the budget and schedule for your ad group, the next step is to define the options in Bidding & Optimization (see Figure 10). This step determines how the ad will be delivered and optimized for maximum effectiveness. On TikTok, you have the choice between two optimization goals: Click and landing page view. The default and recommended optimization goal is Click, which focuses on driving users to click on the ad and visit the website. Alternatively, the landing page view optimization goal targets users who not only click on the ad but also waits for the landing page to load completely. This is particularly useful for campaigns that require users engagement with the content on the landing page.

Figure 10: Defining bidding and optimization options.

Furthermore, you also have the option to select between two bid strategies: lowest cost and bid cap. The lowest cost bidding strategy is a spend-based approach, where TikTok’s algorithm automatically adjusts the bid to generate as many results as possible at the lowest cost per result. This strategy is ideal for most campaigns. However, if you want to have more control over the budget allocation, you can choose the bid cap option. With bid cap, you can set a maximum bid amount for each link click, ensuring that the cost per result remains below your predetermined limit. This strategy can be particularly beneficial for campaigns with limited budgets or when you want to ensure that you are not overpaying for each link click.

Ad level

On the ad level, you can create a captivating advertisement that will be displayed to the target audience. The goal is to make the ad highly appealing and drive engagement among your target population. To achieve this, your ad should be eye-catching, and effectively convey the purpose of the survey. Given that TikTok is a platform centered around videos, we highly recommend incorporating a short and captivating video into your ad. The video should grab viewers’ attention and motivate them to take action. While the ad format can also be a single image or image carousel, it is advisable to include at least some dynamic elements to align with the platform’s style. Moreover, you must keep in mind that TikTok ads are primarily optimized for mobile devices. Hence, the ad should be visually appealing and easily viewable on smartphones, ensuring a seamless user experience.

Figure 11: Example ad design.

In addition, you have the option to choose from various call-to-action buttons, such as “Learn More” or “Sign Up”, which are designed to encourage users to take action. When users click on the button, they will be directed to the survey (see Figure 11). Furthermore, it is important to ensure that the ad text and description convey a clear and concise message that motivates users to participate in the survey.

Once the ad campaign is created, it can be launched. However, it is important to note that TikTok will review the ads to ensure compliance with their guidelines and policies. It is essential to avoid displaying prohibited products or services, such as live animals, adult sexual products, casinos, military gear, or political candidates or parties, in the ad creatives or on the landing pages. A comprehensive list of prohibited content can be found at TikTok (n.d.).

Once the ads are live, you can utilize TikTok’s ad manager tool to track their performance. Monitoring the number of views, clicks, and conversions is essential to assess the reach, engagement, and overall effectiveness of the ads. This valuable information can be used to make necessary adjustments to the ad settings and enhance the performance of future ads.

Ad campaign details

In accordance with the previously discussed hierarchical structure, various settings were configured at the three different levels of the advertising campaign (see Table 1).

At the campaign level, we opted for a consideration advertising objective, specifically “Traffic”, with the aim of driving traffic to our survey website. We set the maximum budget of 400.00 EUR for the entire study. Moving on to the ad group level, we created three distinct ad groups: one targeting English-speaking individuals in the UK (ad group: UK), another targeting German-speaking individuals in Germany (ad group: DE1), and a third focusing on English speaking-individuals in Germany (ad group: DE2). While these ad groups differed in terms of location and language targeting, certain settings were kept consistent across all three: age range, gender, maximum budget per ad group, schedule, delivery, bid strategy, optimization goal, and billing event. For each ad group, we allocated a maximum budget of 130.00 EUR, aiming to evenly distribute our budget across the ad groups. The recruitment phase was scheduled to run from August 6th, 2022, at 16:00 to August 9th, 2022, at 16:00. We configured all ad groups to deliver ads throughout the day, and the optimization goal for each group was to maximize clicks on the advertisements. To minimize costs while maximizing the number of clicks, we selected the “lowest cost” bid strategy. Based on our settings, the billing event was set to cost per click (CPC).

We created a total of 15 ads, with five ads designed under each of the three ad groups. These ads included either a video or an image with animations. As an example, Video 1 illustrates an ad tailored to address the topic of juvenile delinquency, and was specifically targeted to the English-speaking target population. In addition to the visuals, each ad featured our custom identity (@SozPolStudyUniBielefeld), license-free background music provided through TikTok, and concise yet meaningful ad texts, such as “Take our short survey!” in the corresponding language of the target group. To encourage user engagement, we utilized the “Learn more” call-to-action button. When users clicked on this button, they were redirected to our survey page, and we employed URL parameters to track from which respective ad participation in the survey took place.

Campaign results

Table 2 provides an overview of the ad campaign’s performance metrics. Over the course of the 4-day campaign, it reached a total of 117,626 TikTok users. The videos or animated images were played 189,100 times, with the ads being displayed 205,071 times, as reported by the TikTok Ad Manager. The campaign incurred a total cost of 387.48 EUR and generated 3,603 clicks on the call-to-action button, representing a click-through rate of 1.76 percent. The average cost per click was 0.11 EUR, which is comparable to recruitment via other social media platforms like Facebook or Instagram (cf. e.g., Zindel 2022).

Analyzing the ad performance reveals variation depending on the target groups. Ads placed in Germany (ad groups: D1 & D2) reached a higher overall number of impressions compared to the UK. This discrepancy can be attributed to the less developed TikTok ads market in Germany compared to the UK, resulting in lower costs for link clicks due to reduced ad competition. Consequently, the same ad budget resulted in varying levels of impressions. Figure 12 illustrates the cost per click (CPC) by ad group. Initially, during the learning phase of the advertising algorithm, ads in the UK were slightly more expensive than those in Germany. Moreover, the ad group targeting English speakers in Germany (ad group: DE2) exhibited the highest cost per click over the short duration of the campaign. This can be attributed to the relatively lower proportion of TikTok users in Germany who use the platform in English compared to those who use it in German. Targeting a smaller user base incurs higher costs to reach a significant number of users, as reflected in the number of impressions in Table 2.

Figure 12: Average cost per click (CPC) for each ad group.

Examining the metrics distribution in Table 2 highlights significant variations in ad performance. Some ads were displayed and viewed more frequently than others within the same ad group. For instance, in ad group DE2, the ad YD2DE2, one of two ads that focused on the topic of youth delinquency (“YD” for short), had the highest allocation rate determined by the advertising algorithm, resulting in the highest number of clicks on the call-to-action button within this group. When considering the click-through-rate (CTR) for all ads, which represents the conversion rate from impressions to clicks, ads served more frequently tended to have comparatively higher CTRs. However, it is important to note that these ads may not necessarily have the highest CTR within their respective ad groups. For example, in the UK ad group, the ad RF2UK had a CTR of 2.66 percent, surpassing the most frequently displayed ad YD2UK with a CTR of 2.22 percent. This discrepancy arises from the fact that ads that initially garner significant attention in the initial learning phase of the advertising algorithm may not necessarily sustain the highest level of interest from the target audience throughout the entire ad runtime TikTok (n.d.).

Table 3 presents the conversion of link clicks per ad into started and completed interviews, along with the corresponding total cost and cost per completed interview. Out of the 3,603 link clicks, 1,846 (51.29 percent) resulted in started interviews, and a total of 500 interviews (13.88 percent) were completed. With a total campaign cost of 387.48 EUR, the average cost per completed interview was 0.77 EUR. Notably, variations can be observed across the ad groups. The ad group targeting German-speaking individuals in Germany (DE1) received the highest number of link clicks (n=1,676), resulting in the highest number of started interviews (n=983; 58.65 percent), as well as completed interviews (n=265; 15.81 percent). This specific target group was also the most cost-effective to reach, with an average cost of 0.49 EUR per completed interview. In contrast, the average cost per completed interview was 0.99 EUR for the DE2 ad group and 1.23 EUR for the UK group. This disparity highlights the differences in costs discussed earlier, which are influenced by the bidding system and the ad algorithm.

Sample composition

The final sample comprises three subsamples derived from the three different ad groups: English-speaking individuals in the UK (ad group: UK), German-speaking individuals in Germany (ad group: DE1), and English-speaking individuals in Germany (ad group: DE2). Group DE1 accounts for 265 completed interviews, representing 53.00 percent of all interviews. Group DE2 includes 130 interviews (26.00 percent) and group UK has 105 interviews (21.00 percent). The average age of the respondents is 37.22 years (see Figure 13), which is higher than expected considering TikTok’s popularity among younger generations. However, it is important to note that the average age in our sample still falls below the overall average age of 44.7 in the German population (Statistische Ämter des Bundes und der Länder, 2022) and the median age for the UK (UN DESA, 2022).

Figure 13: Age distribution in overall sample.

The average age within the DE1 ad group is 47.74 years, whereas it is 23.99 years for the DE2 ad group and 25.53 years for individuals in the UK. This indicates a strong selection bias towards older individuals in the German-speaking group, while the other ad groups exhibit a strong bias towards younger participants. Table 4 compared the age distribution of each ad group with the ad allocation information provided by TikTok. These selection effects are primarily influenced by the ad algorithm. Therefore, for studies aiming to achieve a demographically balanced age distribution, it is recommended to further stratify ad groups and run specific ads targeted at different age groups to mitigate similar age biases.

Regarding gender distribution, a larger proportion (61.52 percent) of male participants was identified in the sample, while only 27.05 percent identified as female (see Table 5). Additionally, 60 individuals (11.43 percent) in the sample reported identifying with a different gender. A comparison of the sample data with the information from the TikTok Ads-Manager reveals that this effect can also be partially attributed to the allocation of the ads by the advertising algorithm. The DE1 ad group was predominantly delivered to men, while the DE2 and UK ad groups did not exhibit such a strong gender effect in their ad display. It appears that a large proportion of respondents, assumed to be female, chose the third response option, “another gender”. However, it is important to note that we lack individual gender information within the TikTok platform.

The Basics

• Each user has a profile (page) and can add a photo and information about themselves
• Users can follow each other
• Users can tweet, i.e., publicly sharing a text/photo/link
• Each Tweet is restricted to a maximum of 280 characters
• Users can interact with a Tweet via comments (replies), likes, and shares (retweets)
• Users can interact with other users via direct messaging
• Users can create a thread: A series of connected tweets
• Users use hashtags (e.g., #mannheim) in order to associate their tweets with certain topics and to make them easier to find
• Users can search for keywords/hashtags in order to find relevant tweets and users

Twitter in Social Science Research

Analyzing tweets and social interaction on Twitter can help to answer social science research questions, especially in communication research and political science. Contrary to Facebook (API depreciation/shut-down in April 2018) and Instagram (API depreciation/shut-down in December 2018), Twitter data is (easily) accessible for researchers.

According to the Web of Science database, there are 2,598 journal articles published since 2009 with the word “Twitter” in their titles.

Figure 1: Number of articles by year

Figure 2: Number of articles by discipline

The Twitter API Platform

An API (Application Programming Interface) allows users to access (real-time) Twitter data. Twitter offers a variety of API services – some for free, others not. Using these services, one can search for tweets published in the past, stream tweets in realtime, and manage Twitter accounts and ads. The following exercises will focus on the free-of-charge API service, which is used in the vast majority of research projects: The Realtime Streaming API.

The Streaming API - Collecting Tweets in Realtime

“Establishing a connection to the streaming APIs means making a very long lived HTTP request, and parsing the response incrementally. Conceptually, you can think of it as downloading an infinitely long file over HTTP”. This way, we can receive up to a maximum of 1% of all tweets worldwide. As a query is usually specified by selected keywords or geographic areas, you will be able to collect (almost) all relevant tweets for your research interest. There are three filter parameters that you can use:

• ‘Follow’: Receive tweets of up to 5,000 users
• ‘Track’: Receive tweets that contain up to 400 keywords
• ‘Location’: Receive tweets from within a set of up to 25 geographic bounding boxes

API Authentification

You need to authenticate yourself when making requests to the Twitter API. Twitter uses the OAuth protocol, an “open protocol to allow secure authorization in a simple and standard method from web, mobile and desktop applications”. This involves five necessary steps:

• creating a Twitter account
• logging in to your Twitter account via https://developer.twitter.com/
• creating an app
• creating keys, access token & secret

Data Collection

There are a number of ways to collect Twitter data, including writing your own script to make continuous HTTP requests, Python’s tweepy package, and R’s rtweet package. The following demonstrates how to collect Twitter data using different Streaming API endpoints and the rtweet package.

Collecting Tweets Using the rtweet Package

As usual, we start with a little housekeeping: Installing required packages per install.packages() and specifying a working directory using setwd().

# Install Packages
install.packages("rtweet")
install.packages("ggmap")
install.packages("igraph")
install.packages("ggraph")
install.packages("tidytext")
install.packages("ggplot2")
install.packages("dplyr")
install.packages("readr")

# Set Working Directory
setwd("/PATH")

The code chunk below then illustrates three examples of collecting Twitter data using the rtweet package. After loading the required packages into the library(), we specify our authentication token per create_token() (see API Authentification above). After this, we are live: Using the function stream_tweets(), we collect:

1. a sample of current tweets for timeout = 10 seconds.
2. a sample of current tweets containing either of the keywords q = "trump, donald trump" for timeout = 30 seconds.
3. a sample of current tweets for timeout = 180 seconds from a specific location (in this instance, Berlin), restricting our search to a rectangular area defined by coordinates for longitude and latitude. Use, for instance, https://boundingbox.klokantech.com/ to retrieve the coordinates of your choosing.

# Open Libraries
library(rtweet)

# Speficy Authentification Token's provided in your Twitter App
create_token(
  app = "APPNAME",
  consumer_key = "consumer_key",
  consumer_secret = "consumer_secret",
  access_token = "access_token",
  access_secret = "access_secret"
)

# Collect a 'random' sample of Tweets for 10 seconds
stream_tweets(
  q = "",
  timeout = 10,
  file_name = "sample.json",
  parse = FALSE
)
sample <- parse_stream("sample.json")
save(sample, file = "sample_live.Rda")

# Collect Tweets that contain specific keywords for 30 seconds
stream_tweets(
  q = "trump, donald trump",
  timeout = 30,
  file_name = "trump.json",
  parse = FALSE
)
trump <- parse_stream("trump.json")
save(trump, file = "trump_live.Rda")

# Collect Tweets from a specific location
stream_tweets(
  c(13.0883,52.3383,13.7612,52.6755), 
  timeout = 180,
  file_name = "berlin.json",
  parse = FALSE
)
berlin <- parse_stream("berlin.json")
save(berlin, file = "berlin_live.Rda")

By running the steam_tweets() function, we receive Tweets and related meta-information from the Twitter API. The data is stored in .json format (Java Script Object Notation), though we can store these files as data frames after parsing them per parse_stream(). Each row in the data frame represents a Tweet or Re-Tweet and contains, amongst other, the following information:

• The content of a Tweet + Tweet-URL + Tweet-ID
• User-name + User-ID
• Time-stamp
• Place, country, geocodes (rarely)
• User self-description, residence, no. of followers, no. of friends
• URLs to images, videos

For illustration, we take the example of the data frame sample, the result of our first query. As we can see, our 10 second sample without specified key words contains 88 variables (columns) and 359 tweets (rows). You can see the full list of variables below, along with a small anonymized portion of five English language tweets (you can only see the first few characters of each tweet, stored in the variable sample$text).

# Dimensions of the data frame
dim(sample)

## [1] 359  88

# Variables in the data frame
names(sample)

##  [1] "user_id"                 "status_id"              
##  [3] "created_at"              "screen_name"            
##  [5] "text"                    "source"                 
##  [7] "display_text_width"      "reply_to_status_id"     
##  [9] "reply_to_user_id"        "reply_to_screen_name"   
## [11] "is_quote"                "is_retweet"             
## [13] "favorite_count"          "retweet_count"          
## [15] "hashtags"                "symbols"                
## [17] "urls_url"                "urls_t.co"              
## [19] "urls_expanded_url"       "media_url"              
## [21] "media_t.co"              "media_expanded_url"     
## [23] "media_type"              "ext_media_url"          
## [25] "ext_media_t.co"          "ext_media_expanded_url" 
## [27] "ext_media_type"          "mentions_user_id"       
## [29] "mentions_screen_name"    "lang"                   
## [31] "quoted_status_id"        "quoted_text"            
## [33] "quoted_created_at"       "quoted_source"          
## [35] "quoted_favorite_count"   "quoted_retweet_count"   
## [37] "quoted_user_id"          "quoted_screen_name"     
## [39] "quoted_name"             "quoted_followers_count" 
## [41] "quoted_friends_count"    "quoted_statuses_count"  
## [43] "quoted_location"         "quoted_description"     
## [45] "quoted_verified"         "retweet_status_id"      
## [47] "retweet_text"            "retweet_created_at"     
## [49] "retweet_source"          "retweet_favorite_count" 
## [51] "retweet_retweet_count"   "retweet_user_id"        
## [53] "retweet_screen_name"     "retweet_name"           
## [55] "retweet_followers_count" "retweet_friends_count"  
## [57] "retweet_statuses_count"  "retweet_location"       
## [59] "retweet_description"     "retweet_verified"       
## [61] "place_url"               "place_name"             
## [63] "place_full_name"         "place_type"             
## [65] "country"                 "country_code"           
## [67] "geo_coords"              "coords_coords"          
## [69] "bbox_coords"             "status_url"             
## [71] "name"                    "location"               
## [73] "description"             "url"                    
## [75] "protected"               "followers_count"        
## [77] "friends_count"           "listed_count"           
## [79] "statuses_count"          "favourites_count"       
## [81] "account_created_at"      "verified"               
## [83] "profile_url"             "profile_expanded_url"   
## [85] "account_lang"            "profile_banner_url"     
## [87] "profile_background_url"  "profile_image_url"

# An anonymized portion of the data frame, only tweets in English
sample$user_id <- seq(1, nrow(sample), 1)
sample$screen_name <- paste("name", seq(1, nrow(sample), 1), sep = "_")
sample <- subset(sample, lang == "en")
sample[1:5, c("user_id", "created_at", "screen_name", "text", "is_quote", 
              "is_retweet")]

## # A tibble: 5 x 6
##   user_id created_at          screen_name text          is_quote is_retweet
##     <dbl> <dttm>              <chr>       <chr>         <lgl>    <lgl>     
## 1       1 2019-04-03 10:22:32 name_1      just see..<U+2764><U+FE0F>~ FALSE    FALSE     
## 2       4 2019-04-03 10:22:33 name_4      I’m slowy gi~ FALSE    TRUE      
## 3      15 2019-04-03 10:22:33 name_15     "\"I didn't ~ TRUE     FALSE     
## 4      20 2019-04-03 10:22:33 name_20     Hmm. I’ma de~ FALSE    TRUE      
## 5      28 2019-04-03 10:22:33 name_28     I still hear~ TRUE     TRUE

Example 1: Prepping Tweets for Text Analysis

Quantitative Text Analysis (QTA) typically relies on pre-processed textual data (see this blog post for our SSDL workshop on QTA by Denise Traber). The code chunk below illustrates how a collection of Tweets can easily be prepared for QTA techniques such as content or sentiment analysis.

In this example, we use our 30 second sample of Tweets containing the key words “donald trump” and/or “trump”, which we stored as trump_live.RDa (see Collecting Tweets Using the rtweet Package above). Tweet contents are stored in the variable trump$text. Using the tidytextand dplyr packages, we then process the Tweets into a text corpus as follows:

1. Remove URLs from all tweets using gsub()
2. Remove punctuation, convert to lowercase, and seperate all words using unnest_tokens()
3. Remove stop words by first loading a list of stop words from the tidytext package via data("stop_words") and then removing these words from our tweets via anti_join(stop_words)

# Open Libraries
library(tidytext)
library(dplyr)

# Data Cleaning
# Delete Links in the Tweets
trump$text <- gsub("http.*", "", trump$text)
trump$text <- gsub("https.*", "", trump$text)
trump$text <- gsub("&amp;", "&", trump$text)

# Remove punctuation, convert to lowercase, seperate all words
trump_clean <- trump %>%
  dplyr::select(text) %>%
  unnest_tokens(word, text)

# Load list of stop words - from the tidytext package
data("stop_words")

# Remove stop words from your list of words
cleaned_tweet_words <- trump_clean %>%
  anti_join(stop_words)

Following this, we can calculate the word counts in our Tweet collection and visualize the 15 most frequent words:

# Plot the top 15 words
cleaned_tweet_words %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  labs(y = "Count",
       x = "Unique words",
       title = "Count of unique words found in tweets",
       subtitle = "Stop words removed from the list")

## Selecting by n

Figure 3: Top 15 Most Frequent Words

Example 2: Analyzing Locations

Provided that users share their exact geo-information, we can locate their tweets on geographical maps. For example, in our 3 minute sample of Tweets from berlin (see Collecting Tweets Using the rtweet Package above), this information was available for only two Tweets.

The code below demonstrates how one can view these Tweets mapped onto their exact geographical locations. We first pre-process the data, extracting numerical geo-coordinates for longitude and latitude where this information is available. We then subset the data frame to observations where these information exist. Lastly, using the same geo-coordinates as in the data collection, we set up an empty rectangular map of Berlin. Note that the last step involves accessing the Google Maps API, for which you will have to register separately (see, e.g., this post at R-bloggers).

library(ggmap)
library(dplyr)

# Seperate Geo-Information (Lat/Long) Into Two Variables
berlin <- tidyr::separate(data = berlin,
                          col = geo_coords,
                          into = c("Latitude", "Longitude"),
                          sep = ",",
                          remove = FALSE)

# Remove Parentheses
berlin$Latitude <- stringr::str_replace_all(berlin$Latitude, "[c(]", "")
berlin$Longitude <- stringr::str_replace_all(berlin$Longitude, "[)]", "")

# Store as numeric
berlin$Latitude <- as.numeric(berlin$Latitude)
berlin$Longitude <- as.numeric(berlin$Longitude)

# Keep only those tweets where geo information is available
berlin <- subset(berlin, !is.na(Latitude) & !is.na(Longitude))

# Set up empty map
berlin_map <- get_map(location = c(lon = mean(c(13.0883, 13.7612)),
                                   lat = mean(c(52.3383, 52.6755))),
                      zoom = 10,
                      maptype = "terrain",
                      source = "google")

Building up on this, we can then map our collected tweets to see from where they were sent: Berlin-Mitte and Potsdam.

tweet_map <- ggmap(berlin_map)
tweet_map + geom_point(data = berlin,
                       aes(x = Longitude,
                           y = Latitude),
                           color = "red",
                           size = 4,
                           alpha = .5)

Figure 4: GPS Coordinates of 2 Tweets

Bias and Representativity

Twitter users do not represent a random sample from a given population. This is not only due to the presence of bots and company or institutional accounts, but also to the manifold self-selection processes that using Twitter entails:

Population → Internet Users → Twitter Users → Active Twitter Users → Users sharing geo-information

Below are some references that analyze the magnitude and severity of these (and related) problems:

• Barberá, P. & G. Rivero, 2015: Understanding the Political Representativeness of Twitter Users. Social Science Computer Review 33(6) 712-729.
• Mellon, J. & C. Prosser, 2017: Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users. Research and Politics 2017: 1-9.
• Hölig, S., 2018: Eine meinungsstarke Minderheit als Stimmungsbarometer?! Über die Persönlichkeitseigenschaften aktiver Twitterer. M&K Medien- und Kommunikationswissenschaft 66: 140-169.

Replicability and Black-Box Twitter

Real-time Twitter data collection is not reproducible and for a given query. Furthermore, you can only hope that Twitter will provide you with a true random sample of Tweets. If completeness is crucial for your research interest, you will have to pay for complete access to all Tweets ever tweeted: “Both Historical PowerTrack and Full-Archive Search provide access to any publicly available Tweet, starting with the first Tweet from March 2006”.

Data Privacy and Research Ethics

Tweets on public Twitter profiles are generally available. There are no measures in place that prevent the collection and analysis of the data, and users’ consent for the collection and processing of their tweets and profile information is usually not required. This practice is also congruous with certain guidelines for academic and commerical social media research (e.g DGOF Richtlinien zur Social Media Forschung: “In offenen Sozialen Medien bzw. den entsprechenden Bereichen dürfen die personenbezogenen Daten der Teilnehmer grundsätzlich ohne entsprechende explizite Einwilligung auf der Grundlage der gesetzlichen Erlaubnisnorm auch für Zwecke der Markt- und Sozialforschung verarbeitet und genutzt werden.”

However, Twitter’s terms of service are not necessarily congruous with German or EU data protection regulations (e.g. DSGVO). Ultimately, this leaves the ethical and legal questions of how to ensure data privacy to us as researchers. Should we, for instance, further anonymize data, e.g. by separating user IDs from Tweet content and meta-information? What are the implications for open science? Should the full data, including user IDs and geo locations be publicly and permanently shared (e.g., as part of replication materials)? Should the answer to these questions be the same for regular users as opposed to public figures (e.g., politicians)? These questions highlight the urgent need for ongoing discussion about these topics.

Uncertainty of Data Access

One should always have in mind that data access is 100% dependent upon Twitter’s willingness to share the data, and therby also on jurisdiction by which Twitter must abide (think, for instance, about Article 13). Data access for research projects through Facebook’s and Instagram’s APIs has previously been shut-down completely with only few weeks notice. Given that, research projects relying on Twitter data are always risky. This applies particularly to research projects that depends on a constant Twitter data influx over a long period of time (e.g., PhD projects).

Data Storage

Data storage can be an issue when tweets are collected over a long period of time. In many applications, data collections can easily amount to 100-200 GB per month. The use of powerful servers and storage in a relational database (e.g. SQL) are therefore recommended.