This study analyzes media coverage patterns of major electric vehicle models by examining more than 11,000 articles from over 400 news sources published between 2020 and 2024.

The research tracks coverage frequency of five models: the Tesla Model Y, Tesla Model 3, Ford F-150 Lightning, Chevrolet Bolt, and Tesla Cybertruck, correlating coverage patterns with significant industry events.

Some of the most striking findings: Tesla's Model 3 consistently dominated headlines with 30-44% of coverage throughout the study period, but Ford proved it could steal the show – its F-150 Lightning captured an impressive 78% of coverage during its launch. Meanwhile, the Chevrolet Bolt's media presence steadily declined, dropping from 9.79% in 2020 to just 2.15% in 2024, highlighting how quickly the EV media landscape can change. The story that emerges isn't just about vehicles – it's about how automakers navigate the modern media landscape, where a tweet from a CEO can generate as much buzz as a new feature announcement.

Research Objectives

Our investigation aims to:

• Quantify media attention across different electric vehicle models
• Correlate coverage with significant industry events
• Provide insights into technological communication dynamics

Vehicle Specifications Snapshot

First Produced: in 2023

The Tesla Cybertruck is an all-electric pickup with an angular, exoskeleton design. It promises up to 325 miles of range and features a durable stainless-steel body, off-road capabilities, and a towing capacity of over 11,000 pounds.

First Produced: 2020

The Tesla Model Y is an all-electric compact SUV offering a range of up to 337 miles. It features advanced safety systems, autopilot capabilities, fold-flat seats, front and rear trunks, and can tow up to 3,500 pounds.

First Produced: 2017

The Tesla Model 3 is an all-electric sedan with up to 363 miles of range. It features a minimalist design, top safety ratings, and remarkable acceleration, plus a serene cabin with acoustic glass and a panoramic UV-protected roof.

First Produced: 2022

The Ford F-150 Lightning is an all-electric version of the popular F-150 pickup truck. It offers up to 300 miles of range and features dual motors that produce 580 horsepower and 775 lb-ft of torque. The truck can tow up to 10,000 pounds and includes functionality to power homes during electrical outages.

First Produced: 2016

The Chevrolet Bolt EV is an all-electric hatchback produced at GM's Lake Orion Plant through 2023. The first-generation model (2017-2023) features a LFP battery, front-wheel drive, and received a mid-cycle refresh for 2022. Production ended with the 2023 model year. The second-generation Bolt EV is slated for 2026.

2020: The Foundation Year of Electric Vehicle Media Coverage

In 2020, Tesla dominated electric vehicle media coverage by volume, with quantitative analysis showing the Model 3 accounting for 38.4% of coverage, the Model Y at 31.2%, and the Cybertruck at 20.6% - the latter being particularly notable given its pre-production status. This coverage distribution established clear patterns in how media outlets allocated attention across different EV models.

The Chevrolet Bolt EV established its media presence with an 9.79% average share, representing traditional automakers' early efforts in the EV space. This distribution pattern reflected the market's focus on Tesla while maintaining coverage of mainstream manufacturer offerings.

2021: Transition and Market Expansion

The 2021 data shows traditional automakers emerging as significant players in EV media coverage, while Tesla maintained its media dominance.

Key coverage patterns include:

• Tesla Model 3 maintained its position as the most consistently covered vehicle with a 36.6% average attention share.
• Tesla Model Y sustained steady coverage with a 22.6% average.
• The Ford F-150 Lightning's announcement generated significant media interest, achieving a 18.3% average coverage for the year.
• Cybertruck coverage moderated to an 9.97% average.
• Chevrolet Bolt EV increased its presence to 12.5% average, showing growing media interest in mainstream EV offerings.

2022-2024: Coverage Evolution

Our data reveals distinct shifts in media attention from 2022 through early 2024.

The F-150 Lightning commanded significant attention in 2022 with a 40.41% average share, while Tesla models maintained strong presence.

2023 saw more distributed coverage across major models, with the Model 3 leading at 35.21% average.

The narrative shifted again in early 2024 with the Cybertruck's delivery phase generating 38.33% average coverage share. Bolt EV saw a steady decline from 6.67% to 2.15% average coverage.

Coverage Distribution Evolution:

• Strong Tesla presence in early period (2020: Model 3 38.4%, Model Y 31.2%)
• F-150 Lightning emergence in 2022 (40.41% average)
• Cybertruck gaining prominence in 2024 (38.33% average)

Market Coverage Dynamics:

• Consistent strong performance of Tesla Model 3 (averages above 30% through 2023)
• Successful entry of traditional automakers (F-150 Lightning)
• Steady decline in Bolt EV coverage (9.79% in 2020 to 2.15% in 2024)

Coverage Volatility:

• All models show significant coverage variations
• Event-driven spikes remain important throughout the period
• New model launches continue to generate peak coverage moments

Our analysis reveals how different types of events shape media coverage of electric vehicles, with patterns varying significantly by both event type and vehicle model.

Product Launches and Media Response

Product launches consistently generate the most intense media coverage. The F-150 Lightning exemplifies this pattern, dominating coverage during both its reveal (78.3%) and delivery start (78.1%) in 2021 and 2022.

Similarly, Tesla Model Y's production start in 2020 generated significant coverage spikes, though with a different pattern - an initial surge followed by a sharp decline after the announcement phase.

The Cybertruck's 66.67% coverage during its November 2023 delivery event demonstrates how anticipated releases can command significant media attention, particularly after extended development periods.

Production milestones reveal more complex coverage patterns. During the Berlin Gigafactory opening, media attention split between Tesla's models (Model Y at 38.1% and Model 3 at 28.6%) and Ford's F-150 Lightning (33.33%).

The semiconductor shortage in 2021 affected coverage across all manufacturers, with particular focus on production delays and adaptations[1]. COVID-19's impact in 2020 created unprecedented coverage patterns, especially around manufacturing adaptations and supply chain resilience. Strategic moves like price reductions tend to generate moderate coverage (20-50% range), suggesting media interest in market strategy extends beyond just production numbers.

Vehicle-Specific Narrative Patterns

Each vehicle shows distinct coverage patterns that reflect its market position. The Model 3's coverage is more variable than might be expected, with only 30% of months falling in the 30-50% range and peaks reaching 66.67% during significant events like the COVID-19 impact.

The F-150 Lightning demonstrates how traditional auto brands can successfully command attention in the EV space, while the Cybertruck shows how anticipation and development updates can maintain media interest even before production.

Conclusion: Beyond Technology - The Complex Media Landscape of EVs

Broader Implications

• Media coverage is driven by a combination of technological advancements and strategic brand positioning.
• Executive leadership visibility plays a substantial role in shaping media narratives.
• Organizations implement distinct communication approaches to establish market presence.
• Industry perception is shaped through a balance of technical innovation and strategic messaging.

News Media Coverage Analysis Framework

Data Collection: NewsDataHub API and Processing Pipeline

NewsDataHub API provides article data, that is processed with Python code and transformed into interactive visualizations using Plotly.

Our system retrieves articles in batches of 2000 per month for the years 2020-2024. This consistent monthly sampling approach helps us stay within API rate limits while ensuring adequate data coverage for analysis.

def _fetch_month(self, start_date, end_date, target_size=2000):
    url = 'https://api.newsdatahub.com/v1/news'
    headers = {
        'X-Api-Key': self.api_key,
        'Content-Type': 'application/json',
        'User-Agent': 'VehicleTrendAnalyzer/1.0'
    }

    base_query = (
        '("tesla model y" OR "model 3" OR "tesla model 3" OR'
        '"chevrolet bolt" OR "f-150 lightning" OR '
        'cybertruck OR "model 3 highland" OR '
        '"bolt euv" OR "ford lightning" OR "f150 lightning" OR "f 150 lightning" )'
    )

    all_articles = []
    cursor = None

    while len(all_articles) < target_size:
          params = {
                'start_date': start_date,
                'end_date': end_date,
                'language': 'en',
                'q': base_query
            }
            if cursor:
                params['cursor'] = cursor

            try:
                response = requests.get(url, headers=headers, params=params)
                response.raise_for_status()
                data = response.json()

                batch = data.get('data', [])
                if not batch:
                    break

                all_articles.extend(batch)
                cursor = data.get('next_cursor')

                if not cursor:
                    break
            except Exception as e:
                self.logger.error(f"Error fetching batch: {str(e)}")
                break
        time.sleep(0.1)

The implementation includes several key features:

• We retrieve articles in groups of up to 2,000 at a time, making the data more manageable and reducing the load on both our system and the API service.
• A 0.1-second pause between API requests prevents overloading the service by managing request frequency and avoiding server stress from rapid consecutive calls.
• Error logging: The system records any issues through logging. This makes troubleshooting faster and more efficient.
• While we currently use 2000 articles as our target, the system is flexible enough to handle different batch sizes if needed for future analysis.

Vehicle Taxonomy and Keyword Mapping

We implemented a structured classification system (taxonomy) of keywords to systematically identify vehicle model mentions in articles. This taxonomy functions as a reference framework that enables identification of vehicle models across various textual representations:

self.vehicle_taxonomy = {
    'model_y': ['tesla model y', 'model y', 'tesla y'],
    'model_3': ['tesla model 3', 'model 3', 'tesla 3', 'model 3 highland'],
    'bolt_ev': ['chevrolet bolt ev', 'chevy bolt ev', 'bolt ev'],
    'f150_lightning': ['f-150 lightning', 'ford lightning', 'f150 lightning'],
    'cybertruck': ['tesla cybertruck', 'cyber truck']
}

This keyword system is designed to:

• Include different ways people write the same vehicle name (for example, both "F-150" and "F150")
• Catch common abbreviations and alternate names (like "Chevy" instead of "Chevrolet")
• Recognize when articles are talking about the same vehicle even when they use different terms

Data Processing Implementation

Our mention extraction process scans through thousands of articles and tallies each vehicle reference. It works like a digital counter, tracking every time a specific vehicle appears in the text:

def extract_technology_mentions(self, articles):
    mentions = defaultdict(lambda: defaultdict(int))

    for article in articles:
        month = article['pub_date'][:7]
        text = f"{article['title']} {article['description']}"
        text = text.lower()

        for category, keywords in self.vehicle_taxonomy.items():
            if any(keyword in text for keyword in keywords):
                mentions[month][category] += 1

The processing includes several steps:

• Uses defaultdict, a specialized dictionary subclass from Python's collections module (official docs) that automatically creates necessary entries for new months and vehicle types without explicit initialization, making data processing more robust and efficient. This eliminates the need for manual entry handling and reduces potential errors when tallying vehicle mentions across time periods.
• Combines the article's headline and description to check for vehicle mentions in both places.
• Converts all text to lowercase so we don't miss mentions due to capitalization differences.
• Groups the mentions by month to track changes over time.

All Vehicles at Once

Pros:

• Fewer API calls: Let's say you're tracking 5 different vehicles. With individual queries, you'd need to make 5 separate API calls to get data for each vehicle. With a single query for all vehicles at once, you only need 1 API call to get the same data - that's an 80% reduction in API calls.
• Consistent data sampling: When querying all vehicles at once, we analyze each vehicle against the same article set. This ensures fair comparison - if an article mentions multiple vehicles, we capture all mentions from that single source, avoiding sampling bias that could occur from separate queries.
• Simpler rate limiting: Single queries for all vehicles reduce API calls, helping stay within usage limits.

Cons:

• Less query flexibility: it's harder to customize searches for individual vehicles.
• Single point of failure: if something goes wrong with the query, it affects data collection for all vehicles at once.

Individual Vehicle Queries

Pros:

• Better query control: You can fine-tune the search parameters for each vehicle independently, allowing for more precise data collection when needed.
• Isolated failures: If one query fails, it only affects data collection for that specific vehicle. Other vehicles' data collection continues uninterrupted.
• Easy to modify vehicle list: You can add or remove vehicles from your tracking list without affecting queries for other vehicles. This makes the system more flexible for future changes.

Cons:

• More API calls needed: With separate queries for each vehicle, you'll use up your API quota faster. For instance, tracking 5 vehicles requires 5 times as many API calls as a combined query.

Logging Implementation

Our logging system tracks API activity and errors through console and file outputs to help with troubleshooting.

 def setup_logging(self):
        """Configure logging with both file and console output"""
        logger = logging.getLogger('vehicle_trend_analyzer')
        logger.setLevel(logging.INFO)

        # File handler with detailed format
        fh = logging.FileHandler(f'vehicle_trends_{datetime.now().strftime("%Y%m%d_%H%M%S")}.log')
        fh.setLevel(logging.INFO)

        fh_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
        fh.setFormatter(fh_formatter)

        # Console handler with simpler format
        ch = logging.StreamHandler(sys.stdout)
        ch.setLevel(logging.INFO)
        ch_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
        ch.setFormatter(ch_formatter)

        logger.addHandler(fh)
        logger.addHandler(ch)
        return logger

It's important to use logging because it creates an audit trail of API interactions while providing real-time monitoring through dual console and file output, with timestamped records to track issues.

Attention Share Calculation

We calculate what percentage of the total coverage each vehicle receives, similar to calculating market share:

def calculate_attention_shares(self, mentions):
    attention_shares = {}

    for month, categories in mentions.items():
        total_mentions = sum(categories.values())

        if total_mentions > 0:
            shares = {
                category: (count / total_mentions * 100)
                for category, count in categories.items()
            }
            attention_shares[month] = shares

This calculation:

• Converts raw mention counts into percentages for easier comparison
  

  Consider this example: If Tesla receives 50 mentions in both January and February, but January had 100 total articles while February had 500, the raw numbers appear equal. Yet Tesla actually dominated January with 50% of coverage while only capturing 10% in February. Converting to percentages reveals this important distinction.

• Accounts for months where the total number of articles varies

News volume naturally fluctuates - some months might have major auto shows or product launches generating lots of articles, while others are quieter. By adjusting for these variations, we ensure that a "busy news month" doesn't artificially inflate a vehicle's importance in our analysis.

• Creates metrics that can be compared across different time periods

This standardization methodology enables consistent comparison across varying sample sizes, ensuring that fluctuations in total article volume do not distort the relative coverage analysis.

• Helps identify which vehicles are receiving more or less media attention

By tracking these percentages over time, we can spot important trends - like when a new vehicle starts gaining more media attention or when interest in an established model begins to fade. This helps us understand how public and media interest evolves in the electric vehicle market.

We maintain a timeline of significant industry events to help understand changes in media coverage. While this timeline is not exhaustive, it captures major milestones that could potentially influence coverage patterns:

self.significant_events = {
    '2020-01': 'Tesla Model Y begins volume production',
    '2020-04': 'COVID-19 pandemic impacts automotive industry',
# ... additional events
}

This timeline serves several purposes:

• Records major events that might influence media coverage
• Helps explain sudden changes in how much attention different vehicles receive
• Provides context for interpreting coverage patterns
• Connects media coverage to real-world industry developments

Our research framework provides opportunities for several key areas of future investigation.

Implementation of advanced semantic analysis would enable better understanding of media coverage through context-aware mention detection and machine learning integration, improving vehicle reference identification accuracy.

Future research should expand beyond traditional news media to include social media analytics and financial market data, while broadening geographic coverage to analyze regional variations.

Developing computational sentiment analysis would help assess coverage tone and track discourse evolution across different publication types and audiences.

Critical Methodological Considerations and Data Interpretation

Our research leveraged the NewsDataHub API to track electric vehicle media coverage, processing over thousands of news articles. The analysis reveals both the power and inherent challenges of sifting through large number of news data to gain insights.

There are several important considerations and limitations of such analysis to keep in mind:

Data Coverage and Interpretation

• Coverage percentages represent relative mentions among selected models, not absolute global coverage.
• Monthly zero mentions indicate absence within our taxonomic approach, not complete media invisibility.
• It is possible that there are potential geographical or language biases in coverage.
• Analysis relies solely on NewsDataHub API, which may not capture all global news sources.
• Our list of significant events does not include all events that might have affected the news coverage.

Technical Constraints

• Our keyword-based detection system operates with fundamental constraints.
• Monthly aggregation may obscure short-term fluctuations (e.g., a significant event's coverage might get averaged out).
• We cannot definitively establish causal relationships between events and coverage spikes.
• There is a risk of misclassification (e.g., articles mentioning "Tesla" in broader context)
• This is a time-bound study (2020-2024) focused on five specific models.

Source and Detection Dynamics

• NewsDataHub's database, while extensive, does not represent a complete global news landscape.
• This analysis only included English-language articles, which means it may have missed coverage from regional publications and non-English sources.
• Our keyword-matching method must balance finding all relevant articles while avoiding false matches.
• Strict taxonomic rules may lead to both false positives and negatives.

If you use data or findings from this analysis in your own work, please cite as:

All data and analysis presented in this article are derived from NewsDataHub's proprietary database and API. When using this content, please ensure appropriate attribution to NewsDataHub.