Search Engine Results Research Articles

Optimizing online visibility: A comprehensive study on effective SEO strategies and their impact on website ranking

This study aims to have a more diversified view of the online visibility through attempting to evaluate the effectiveness of various SEO strategies in placing on website in search engine result. This research involves 400 respondents where it checks how keywords as one of the SEO strategies affect website ranking as well as technical SEO and off-page strategies. The appropriateness of the relevant keyword, as the result shows, there is a significant connection with the website ranking, closely trailing the importance of technical SEO in positioning the website on the first page, exerting a pronounced impact. While off-page strategies are the third most dazzling one: a significant degree of its residence/impact on website ranking. This research is a significant contribution to the field of digital marketing and its literature as it delivers an in-depth understanding on the major factors that affects online visibility and website ranking.

Optimizing online visibility: A comprehensive study on effective SEO strategies and their impact on website ranking

This study aims to have a more diversified view of the online visibility through attempting to evaluate the effectiveness of various SEO strategies in placing on website in search engine result. This research involves 400 respondents where it checks how keywords as one of the SEO strategies affect website ranking as well as technical SEO and off-page strategies. The appropriateness of the relevant keyword, as the result shows, there is a significant connection with the website ranking, closely trailing the importance of technical SEO in positioning the website on the first page, exerting a pronounced impact. While off-page strategies are the third most dazzling one: a significant degree of its residence/impact on website ranking. This research is a significant contribution to the field of digital marketing and its literature as it delivers an in-depth understanding on the major factors that affects online visibility and website ranking.

Enhancing Image SEO using Deep Learning Algorithms: A Research Approach

Visual content influences profoundly user interactions and search engine rankings. Traditional SEO techniques, while foundational, often fall short in the swiftly changing search landscape. This paper presents a cutting-edge solution that leverages the power of deep learning and generative AI to redefine image SEO. Through the innovative integration of the AWS Rekognition's image analysis and ChatGPT's advanced natural language processing, we automate and refine the creation of alt-texts, ensuring they are not only precise, but also SEO-enriched. The result is a harmonious blend of technology and strategy that significantly boosts online content discoverability. Using a specific website as a test environment, the proposed methodology demonstrated a profound impact on SEO performance. Initially, no images on the webpage had alt-texts. After applying the AI-driven approach, all images were equipped with descriptive, SEO-optimized alt attributes, markedly improving their visibility in search engine results. These results underscore the effectiveness of integrating advanced AI technologies in SEO strategies, providing a scalable and effective framework for enhancing digital content accessibility and search engine rankings.

Market or Markets? Investigating Google Search’s Market Shares under Vertical Segmentation

Is Google Search a monopoly with gatekeeping power? Regulators from the US, UK, and Europe have argued that it is based on the assumption that Google Search dominates the market for horizontal (a.k.a. “general”) web search. Google disputes this, claiming that competition extends to all vertical (a.k.a. “specialized”) search engines, and that under this market definition it does not have monopoly power. In this study we present the first analysis of Google Search’s market share under vertical segmentation of online search. We leverage observational trace data collected from a panel of US residents that includes their web browsing history and copies of the Google Search Engine Result Pages they were shown.We observe that participants’ search sessions begin at Google greater than 50% of the time in 24 out of 30 vertical market segments (which comprise almost all of our participants’ searches). Our results inform the consequential and ongoing debates about the market power of Google Search and the conceptualization of online markets in general.

An Analysis Method for the Impact of GenAI Code Suggestions on Software Engineers’ Thought Processes

Interactive generative AI can be used in software programming to generate sufficient quality of code. Software developers can utilize the output code of generative AI as well as website resources from search engine results. In this research, we present a framework for defining states of programming activity and for capturing the actions of developers in a time series. We also describe a scheme for analyzing the thought process of software developers by using a graph structure to describe state transitions. By applying these means, we showed that it is feasible to analyze the effects of changes in the development environment on programming activities.

Examining the importance of digital marketing strategies in brand creation for SMEs in Malaysia

This research aimed to investigate the relationship between brand creation and various types of marketing strategies in SMEs across Malaysia, with a focus on the role of digital platforms in shaping consumer engagement and perceptions. The research was conducted using a correlational quantitative approach in Malaysia. Through the probability sampling method, 51 SME employees were selected. Research data were collected through surveys and analyzed using SPSS software. The findings showed that utilizing various digital marketing strategies was essential for SME brand creation. Social media, content creation, community engagement, influencer collaboration, SEO, and email marketing proved significant in increasing brand visibility and loyalty among Malaysian consumers. The study highlighted that content marketing enabled SMEs to convey their values and unique selling propositions. Additionally, implementing an SEO strategy significantly increased an SME's visibility in search engine results, boosted website traffic, and built customer credibility. Influencer marketing was also effective for Malaysian SMEs in facilitating audience reach and increasing brand awareness through personalized consumer relationships. Moreover, email marketing played an important role in brand creation by enhancing customer relationships and engagement. This study provided empirical insights into how Malaysian SMEs leveraged digital marketing channels to strengthen their brands, emphasizing the need to adapt strategies to changing consumer preferences and technological advances in the digital landscape.

Research on Algorithmic Gender Bias under the Paradigm of Machine Behavior Studies

In the field of communication studies, machine behavior specifically refers to information dissemination activities involving artificial intelligence technology. As algorithms increasingly become the primary force in information dissemination, their potential gender bias becomes increasingly apparent. This paper, based on three research scopes in machine behavior studies: individual behavior, collective behavior, and human-machine interaction behavior, examines the gender bias exhibited by artificial intelligence entities in algorithms at these three levels. At the individual behavior level, the tendency of algorithm development to simplify features overlooks the diversity present in female society. The inherited data bias and human bias make it difficult to avoid gender discrimination. At the collective behavior level, the creation of opinion leader-type social robots expands the subject of information fog, making the concealed gender discrimination against women more covert. The use of large-scale machine armies manipulates search engine results, leading to severe gender bias in search engine outputs. At the hybrid human-machine behavior level, artificial intelligence shapes female images to construct female cognitive thinking. Algorithms acquire human bias during interaction with users, and social robots amplify gender bias issues through mixed human-machine behavior.

Search Engines and Generative Artificial Intelligence Integration: Public Health Risks and Recommendations to Safeguard Consumers Online.

The online pharmacy market is growing, with legitimate online pharmacies offering advantages such as convenience and accessibility. However, this increased demand has attracted malicious actors into this space, leading to the proliferation of illegal vendors that use deceptive techniques to rank higher in search results and pose serious public health risks by dispensing substandard or falsified medicines. Search engine providers have started integrating generative artificial intelligence (AI) into search engine interfaces, which could revolutionize search by delivering more personalized results through a user-friendly experience. However, improper integration of these new technologies carries potential risks and could further exacerbate the risks posed by illicit online pharmacies by inadvertently directing users to illegal vendors. The role of generative AI integration in reshaping search engine results, particularly related to online pharmacies, has not yet been studied. Our objective was to identify, determine the prevalence of, and characterize illegal online pharmacy recommendations within the AI-generated search results and recommendations. We conducted a comparative assessment of AI-generated recommendations from Google's Search Generative Experience (SGE) and Microsoft Bing's Chat, focusing on popular and well-known medicines representing multiple therapeutic categories including controlled substances. Websites were individually examined to determine legitimacy, and known illegal vendors were identified by cross-referencing with the National Association of Boards of Pharmacy and LegitScript databases. Of the 262 websites recommended in the AI-generated search results, 47.33% (124/262) belonged to active online pharmacies, with 31.29% (82/262) leading to legitimate ones. However, 19.04% (24/126) of Bing Chat's and 13.23% (18/136) of Google SGE's recommendations directed users to illegal vendors, including for controlled substances. The proportion of illegal pharmacies varied by drug and search engine. A significant difference was observed in the distribution of illegal websites between search engines. The prevalence of links leading to illegal online pharmacies selling prescription medications was significantly higher (P=.001) in Bing Chat (21/86, 24%) compared to Google SGE (6/92, 6%). Regarding the suggestions for controlled substances, suggestions generated by Google led to a significantly higher number of rogue sellers (12/44, 27%; P=.02) compared to Bing (3/40, 7%). While the integration of generative AI into search engines offers promising potential, it also poses significant risks. This is the first study to shed light on the vulnerabilities within these platforms while highlighting the potential public health implications associated with their inadvertent promotion of illegal pharmacies. We found a concerning proportion of AI-generated recommendations that led to illegal online pharmacies, which could not only potentially increase their traffic but also further exacerbate existing public health risks. Rigorous oversight and proper safeguards are urgently needed in generative search to mitigate consumer risks, making sure to actively guide users to verified pharmacies and prioritize legitimate sources while excluding illegal vendors from recommendations.

How much freedom does an effectiveness metric really have?

AbstractIt is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into. In fact that second freedom is, to a considerable degree, illusory. That is because if one SERP in a pair has been given a certain score by a metric, fundamental ordering constraints in many cases then dictate that the score for the second SERP must be either not less than, or not greater than, the score assigned to the first SERP. We refer to these fixed relationships as innate pairwise SERP orderings. Our first goal in this work is to describe and defend those pairwise SERP relationship constraints, and tabulate their relative occurrence via both exhaustive and empirical experimentation. We then consider how to employ such innate pairwise relationships in IR experiments, leading to a proposal for a new measurement paradigm. Specifically, we argue that tables of results in which many different metrics are listed for champion versus challenger system comparisons should be avoided; and that instead a single metric be argued for in principled terms, with any relationships identified by that metric then reinforced via an assessment of the innate relationship as to whether other metrics are likely to yield the same system‐versus‐system outcome.

Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

Search engines are now the main source for information retrieval due to the hugeexpansion of data on the internet over the last ten years. Providing users with the mostrelevant results for their queries poses a significant challenge for search engines.Semantic search engines, which go beyond traditional keyword-based searches, haveappeared as advanced information retrieval systems to address this problem. Thesesearch engines produce more precise and pertinent search results because theyunderstand the meanings of words and their relationships. They play a pivotal role inmanaging the vast amount of internet data, with a primary aim of enhancing searchprecision and user satisfaction. However, improving search precision remains as animportant goal for natural language processing researchers. The main objective of ourresearch is to improve the search engine results. We present a novel approach formeasuring the similarity between a user’s query and a list of documents within a searchengine. This approach provides a new fuzzy recommendation system using a syntacticand semantic similarity. Our results indicate that our method outperforms severalexisting approaches from the literature, achieving a high level of accuracy.

USER EVALUATION-DRIVEN RANKING CONCEPT

Context. The problem of personalizing search engine results, empowering users with search result management tools and developing new ranking models based on user’s subjective information needs. The object of the study was to modeling information search results in the Internet based on user ratings.
 Objective. The goal of the work is to form unique expert groups for each user, based on calculating the measure of agreement between the current user’s opinions and potential experts.
 Method. Introducing a novel method for ranking search results based on user ratings, which takes a subjective approach to the ranking process. This approach involves the formation of distinct expert groups tailored to individual users. Experts are selected based on the level of agreement between their opinions and the current user, determined by shared ratings on a specific set of web resources. User selection for the expert group is based on their weight relative to the current user, serving as a measure of agreement.
 The proposed methodology offers a fresh approach to forming unique expert groups for each user, utilizing three different strategies depending on the presence of shared ratings on a particular set of web resources between the user and potential experts.
 The developed ranking method ensures that each user receives a personalized list of web resources with a distinct order. This is accomplished by incorporating unique ratings from the expert group members associated with each user. Furthermore, each rating contributes to the ranking model of web resources with an individual weight, calculated based on an analysis of their past system activity.
 Results. The developed methods have been implemented in software and investigated for complex web data operation in real time.
 Conclusions. The conducted experiments have confirmed the effectiveness of the proposed software and recommend its practical use for solving complex web data operation in real time. Prospects for further research may include optimizing software implementations and conducting experimental investigations of the proposed methods on more complex practical tasks of various nature and dimensions

An Empirical Evaluation of Information Sharing's Impact on Profitability; Evidence from the Solar Sector

The role of online marketing is significant. Businesses strive to maximize their profit, and there are low-budget but efficient digital solutions for this aim. Social media sites are not just a connection point but also a great surface to collect information regarding products and/or companies. Even though these free opportunities are often used, websites are the „classical” standalone digital surfaces used for marketing purposes. SEO (Search Engine Optimization) provides various techniques to improve the companies' websites' SERP (Search Engine Result Page). In addition, it could provide even statistically proven financial benefits. The current study analyzes the SEO’s influence on the financial performance of SMEs in the solar sector. The nationwide study based on the Kruskal-Wallis test revealed the importance of connecting social media sites to the company's website. The proper Social SEO results in significantly higher after-tax profit. Regarding the first contentful paints (first feedback to the browser about the website loading), the Pearson correlation coefficients showed up moderately strong, positive, significant relationships with many financial indicators.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Analyzing Cognitive Load Associated with Manual Text Classification Task Using Eye Tracking

The accuracy of machine learning-based automated text classification systems, such as spam filters and search engine results, heavily depends on the quality of manual text classification. However, the cognitive demands of manual text classification tasks, particularly when dealing with challenging or difficult-to-comprehend texts, have not been extensively explored in previous studies. This research aims to address this gap by investigating the cognitive load associated with manual text classification tasks through analyzing eye tracking data. In this study, 30 participants performed manual text classification tasks while their ocular parameters were recorded using an eye tracker. The findings of this study revealed that ocular parameters recorded through eye tracking provided valuable insights into the cognitive load experienced during manual text classification tasks. Furthermore, it was observed that complex narratives led to higher cognitive load estimation. Moreover, native English-speaking participants exhibited lower cognitive load, compared to non-native English speakers.

The Role of Search Engine Optimization in Determining Journalism Quality

The SEO strategy for news content is characterized by employing mass media around an issue and then creating it frequently by writing as much as possible. Current event keywords are constantly incorporated into news content. Finally, the news will appear near the top of search engine results. Writing adapts to culture, particularly by observing text dynamics. Remember to evaluate editorial efforts in order to maintain journalistic quality. Data gathered through in-depth interviews, observations, and documentation. According to the findings of this study, all four media surveyed believe that SEO is a method of increasing traffic so that advertisements can develop. Doing SEO and then showing politically on the top page of Google search also puts other leading media's excellent reputation at danger. Publishers also develop regional content and implement target page view policies to ensure that the editorial staff adheres to the SEO algorithm. Surprisingly, there are limits on how SEO publishers may provide great information.

Implementasi White Hat SEO (Search Engine Optimization) Pada Website E-Commerce Sayur Plagro.id

Internet users in Indonesia have increased by 64% of the total population in Indonesia in 2020. This number has increased by 17% compared to 2019. The website is one of the internet technologies that is not only a medium of information but also to support a company's business processes such as selling products and services online, but sales through the website cannot be done without the support of the right promotion strategy. Usually, 62% of internet users are interested in the first 10 or 20 pages of the SERP (Search Engine Result Page), and another 38% will look at the website on the next page. One method that can be used to get top rankings on search engines on the internet is to implement SEO methods. SEO (Search Engine Optimization) is a promotional technique that uses search engine optimization to ensure that a website ranks high or is on the first page on SERPs. One of the problems faced by the Plagro.id e-commerce website is the position in search engine search results based on the targeted keywords not being in an indexed position on the SERP (Search Engine Results Page) or ranked 100 and above on the SERP (Search Engine Results) Page), so that it is not visible or is still on a page far from the SERP. This study implemented one of the SEO methods, namely White Hat SEO on the Plagro.id e-commerce website, then tested using Serprobot Tools. After the website is optimized, it is tested again with the same testing tools. From the results of this study, the results of increasing the ranking of the Plagro.id e-commerce website in the SERP after implementing SEO.

Search engine discourse analysis: how ‘shadowban’ affects policy

ABSTRACT Amidst company denials, public interest in ‘shadowbans’ has grown. Without notifying the creator, ‘shadowbans’ limit the visibility of user-generated content. Shadowbans can be an effective content moderation technique, although they are subject to the same policy concerns. Existing research has focused on technologically demonstrating shadowbans, while other research has focused on people’s experiences, including how social media users use the word ‘shadowban’ to call for platform accountability. However, less research has focused on media discourses. Thus, I ask: what does ‘shadowban’ do in search engine results? Using Search Engine Discourse Analysis (SEDA) of 107 search engine results, I argue that when incorporated into media discourses returned via search engine, ‘shadowban’ maintains existing power dynamics through attributions of agency, strategically managing conflict, and producing markets, all of which have the potential to shape policy debates. Future discussions of ‘shadowbans’ should critically attend to power differentials in the public sphere. Moreover, if governance is legitimized through media, this article demonstrates how critical analysis of how media sources frame platform governance is an important line of inquiry.

Future-proofing Search Engine Marketing: An Empirical Investigation of Effects of Search Engine Results on Consumer Purchase Decisions

ABSTRACT A popular joke amongst marketers is that the best place to hide a dead body is page two of search results. Meaning, nobody looks beyond the first page of search results. In a cookie-free future, search engines double down on how marketers communicate expertise, authority, and trust. This pushes brands to shift away from product-focused search engine marketing, toward an intent-based approach. To stay at the top of search engine results, marketers must understand consumer assessment of search results. Guided by an integrated framework combining UTAUT2, theory of reasoned action, and media richness theory, this empirical study used a 2 (search result type: organic vs. sponsored) × 3 (presentation format: text-only vs. image vs. video) between-subjects research design for assessing the effects of search results on consumer decisions. Results help establish the connection between consumer perceptions of search results and purchase decisions. Implications for AI-driven search engine marketing are presented.

Dynamicity of Web Services and Web Scrapping

This research focuses on dynamic Web service link searching using web scraping (WS). WS is method of extracting required contents from search engine results. The proposed system inputs the functional word to search engine and retrieves required Web services from search engine results. WS method retrieves the links of the Web Service Definition Language from which service and operation elements are extracted and processed to check suitability of Web services. Matched Web services will be clustered and invoked. This research is implemented using J2EE and remote Web services are executed according to user requirement. Analysis of results justified that this will be a suitable search method to search for remote Web services and to execute using open source software.

The search query filter bubble: effect of user ideology on political leaning of search results through query selection

ABSTRACT It is commonly assumed that personalization technologies used by Google for the purpose of tailoring search results for individual users create filter bubbles, which reinforce users’ political views. Surprisingly, empirical evidence for a personalization-induced filter bubble has not been forthcoming. Here, we investigate whether filter bubbles may result instead from a searcher’s choice of search queries. In the first experiment, participants rated the left-right leaning of 48 queries (search strings), 6 for each of 8 topics (abortion, benefits, climate change, sex equality, immigration, nuclear family, Islam, and taxation). An independent sample of participants were then asked to select one of these queries for each of the 8 topics. With the exception of the topic of Islam, participants were significantly more likely to select a query corresponding to their own political leaning, compared to other queries, explaining between 12% and 39% of the variance. A second experiment investigated the effect of the political leaning of the same queries on the overall political leaning of Search Engine Result Pages (SERPs) in Google Search. The top six results of each SERP were rated collectively by a third group of participants, explaining 36.3% of the variance across all 48 search terms (p < .00001). That is, (1) participants in our experiments tended to select own-side search queries, and (2) using those queries tended to yield own-side search results when using the Google search engine. Our results are consistent with the notion of a self-imposed filter bubble in which query selection plays a salient role.