scholarly journals Online Information Seeking Behavior among Indonesian during the COVID-19 Pandemic

2021 ◽  
pp. 1-7
Author(s):  
Sulistyawati Sulistyawati ◽  
Herman Yuliansyah ◽  
Surahma Asti Mulasari ◽  
Tri Wahyuni Sukesi
Keyword(s):  
Information Seeking ◽  
Information Needs ◽  
Herd Immunity ◽  
Immunization Coverage ◽  
Online Information ◽  
Online Behavior ◽  
The Public ◽  
Seeking Behavior ◽  
Data Source ◽  
Online Information Seeking

Background: The COVID-19 vaccination movement in Indonesia until mid-2021 has still not reached the expected number, which is less than 20% of the Indonesian population who receive the first dose of vaccine. Meanwhile, the government's target for accelerating herd immunity is to achieve immunization coverage of 70% by November 2021. Several issues have arisen regarding the public's reluctance to accept the COVID-19 vaccine, such as vaccine readiness, efficacy, conspiracy, halal issues, and the negative side of this vaccine. Aims: This study aims to assess the online behavior of the Indonesian community in seeking information related to the COVID-19 vaccine. Methods: Google trends was used as a data source to see the public interest in several issues including COVID, vaccines, and government policies. Analysis: The analysis was carried out descriptively. Results: The results of this study indicate the movement over time of society towards several terminologies in line with events or the implementation of policies in Indonesia. Conclusion: This research contributes to surveillance of the public's information needs on the COVID-19 issue to avoid misinformation.

Tips for Tracking Web Information Seeking Behavior

2011 ◽  
pp. 89-118
Author(s):  
Brian Detlor ◽  
Maureen Hupfer ◽  
Umar Ruhi
Keyword(s):  
Individual Differences ◽  
Information Seeking ◽  
Information Seeking Behavior ◽  
Online Information ◽  
Tracking Data ◽  
Portal Site ◽  
Making Sense ◽  
Web Information ◽  
Seeking Behavior ◽  
Online Information Seeking

This chapter provides various tips for practitioners and researchers who wish to track end-user Web information seeking behavior. These tips are derived in large part from the authors’ own experience of collecting and analyzing individual differences, task, and Web tracking data to investigate people’s online information seeking behaviors at a specific municipal community portal site (myhamilton.ca). The tips discussed in this chapter include: (1) the need to account for both task and individual differences in any Web information seeking behavior analysis; (2) how to collect Web metrics through deployment of a unique ID that links individual differences, task, and Web tracking data together; (3) the types of Web log metrics to collect; (4) how to go about collecting and making sense of such metrics; and (5) the importance of addressing privacy concerns at the start of any collection of Web tracking information.

Using Wikipedia to Predict Election Outcomes

2017 ◽  
Vol 81 (3) ◽  
pp. 714-735 ◽  
Author(s):  
Benjamin K. Smith ◽  
Abel Gustafson
Keyword(s):  
Public Opinion ◽  
Information Seeking ◽  
Online Information ◽  
Election Forecasting ◽  
Seeking Behavior ◽  
General Elections ◽  
Election Results ◽  
Using Data ◽  
Us Senate ◽  
Online Information Seeking

Abstract This study seeks to improve election forecasting by supplementing polling data with online information-seeking behavior trends as an indicator of public opinion. Aggregate trends of demonstrations of interest or engagement have been shown to accurately predict behavior trends and reflect public opinion. Further, because traditional poll-based predictions are inherently undermined by self-reporting biases and the intention-behavior disconnect, we can expect that information-seeking trends on widely used social media—as an autonomous and unobtrusive indicator of relative levels of public opinion—can help correct for some of this error and explain unique, additional variance in election results. We advance the literature by using data from Wikipedia pageviews along with polling data in a synthesized model based on the results of the 2008, 2010, and 2012 US Senate general elections. Results show that Wikipedia pageviews data significantly add to the ability of poll- and fundamentals-based projections to predict election results up to 28 weeks prior to Election Day, and benefit predictions most at those early points, when poll-based predictions are weakest.

Tips for Tracking Web Information Seeking Behavior

2010 ◽  
pp. 256-283
Author(s):  
Brian Detlor ◽  
Maureen Hupfer ◽  
Umar Ruhi
Keyword(s):  
Individual Differences ◽  
Information Seeking ◽  
Information Seeking Behavior ◽  
Online Information ◽  
Tracking Data ◽  
Portal Site ◽  
Making Sense ◽  
Web Information ◽  
Seeking Behavior ◽  
Online Information Seeking

This chapter provides various tips for practitioners and researchers who wish to track end-user Web information seeking behavior. These tips are derived in large part from the authors’ own experience of collecting and analyzing individual differences, task, and Web tracking data to investigate people’s online information seeking behaviors at a specific municipal community portal site (myhamilton.ca). The tips discussed in this chapter include: (1) the need to account for both task and individual differences in any Web information seeking behavior analysis; (2) how to collect Web metrics through deployment of a unique ID that links individual differences, task, and Web tracking data together; (3) the types of Web log metrics to collect; (4) how to go about collecting and making sense of such metrics; and (5) the importance of addressing privacy concerns at the start of any collection of Web tracking information.

Functional Model to Online Information Seeking

2020 ◽  
pp. 28-37
Author(s):  
Ashwani Kumar
Keyword(s):  
Information Seeking ◽  
Functional Model ◽  
Information Seeking Behavior ◽  
Online Information ◽  
Online Platform ◽  
Proposed Model ◽  
Seeking Behavior ◽  
Online Information Seeking

This chapter focuses on developing a Functional Model of Online Information Seeking Behavior of Academicians to the effective seeking of an online platform. The unavailability of standard, uniform, and multilingual supportive model is the major reason to prepare this proposal. The proposed model will help in an enhancement of the utilization of the databases provided by government-funded agencies. The main aim of this chapter is to explore the online information seeking behavior and provide a functional model to effective seeking approaches for the academicians.

Competitive Frames and Accuracy Motivations

2019 ◽  
Vol 31 (3) ◽  
pp. 137-148
Author(s):  
Porismita Borah
Keyword(s):  
Information Seeking ◽  
Civil Liberties ◽  
Framing Effects ◽  
Online Information ◽  
Web Based ◽  
Mediating Role ◽  
Psychological Mechanisms ◽  
Seeking Behavior ◽  
Value Framing ◽  
Online Information Seeking

Abstract. To examine the psychological mechanisms involved in value framing effects, the present study extends previous research on competitive frames and tests the mediating role of ambivalence in value framing effects. The current research delves into the nuances of value framing effects and helps explain processes such as applicability. Two web-based experiments were conducted using value frames related to two different issues, civil liberties and gay rights. Findings from moderated-mediation models indicate that when individuals are exposed to competitive frames they feel ambivalent. Because of this ambivalence, participants were more willing to seek information and showed increased online information-seeking behavior. However, these findings are true only in cases of individuals who are motivated to process the information. Implications are discussed.

Eyes on the frame

2017 ◽  
Vol 27 (2) ◽  
pp. 303-320 ◽  
Author(s):  
Sabrina Heike Kessler ◽  
Lars Guenther
Keyword(s):  
Information Seeking ◽  
Information Needs ◽  
Framing Effects ◽  
Online Information ◽  
Online Search ◽  
Online Searching ◽  
Content Type ◽  
Media Frames ◽  
Online Information Seeking ◽  
Practical Implications

Purpose Using the internet parallel to or after television (TV) consumption changes the way people receive news. The way information is framed by the media has been found to influence the behavior of news recipients. The purpose of this paper is to hypothesize that the exposure to TV media frames would affect a lay audience’s online information-seeking behavior. Design/methodology/approach In an experiment combining eye tracking and content analysis, participants (n=72) were exposed to one of three TV clips with different media frames (based on a full-sample content analysis) that focused on Alzheimer’s disease. After exposure, participants informed themselves about the issue online. Eye tracking allows to investigate whether individuals mainly scan information, or whether they compute information on a higher level of attention (use more thorough deliberate comparison of information and really reading information). Findings Three different frames of online content were identified. Framing was found to influence the individual online searching and reading of information on a descriptive level (entering search words and viewing website content) to some degree, but not on a procedural level (such as selecting online search results). Research limitations/implications This study makes a significant contribution to the literature embedding an established theoretical process like framing effects into the internet literature. Regarding the broader theoretical context, this study shed some light on cross-media framing effects on online behavior. Applying the psychological perspective of framing theory to explain and predict online searching behavior is beneficial for specific types of online search behavior. Main limitations are the not representative student sample and the forced task that participants had to inform themselves about Alzheimer’s disease online. Practical implications The results have practical implications for the creation of TV-related websites. There can be a positive, profitable synergy of TV and online websites. The websites can complement the TV programs with the focus on information needs of the recipients depending on the TV activated audience frames. Therefore, media managers would do well to plan the contents of their websites as internet-based resources that meet the activated information needs. Originality/value This study is among the first to investigate the framing effects of TV on the online information searching behavior of individuals. A deeper understanding of how media frames, especially from TV, are affecting online information seeking will allow researchers to better explain and predict online user behavior and information needs. But still, more research is needed.

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