Browsing by Author "Sharma, Rajesh, juhendaja"

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A Network-Based Model for Television Services Churn Prediction
(Tartu Ülikool, 2021) Käärik, Martin; Sharma, Shakshi, juhendaja; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Predicting churn helps us understand which customers are likely to replace the company’s services with competitors. As the cost of acquiring users is much higher than retaining existing ones, churn prediction has emerged for numerous telecommunication companies as a critical tool to retain an existing customer base. Usually, churn is predicted by modeling individual customers’ behaviour and relatively static features such as demographic data, contractual data, and product information. Recent work has shown that analysing customers’ social network improves the accuracy of churn prediction. Although the network analysis is widely researched for telecommunication customers, little to no research was found for TV service users. This thesis attempts to fill this gap by analysing customers behaviour prior to churning as well as their call logs. Models with and without the network analysis features were trained with XGBoost, Adaboost, Random forest, Logistic regression, and Gradient Boost Classifier. Differences in the prediction results, whether the additional features were added, were presented in this paper. Results indicate that adding information from call logs improves the minority class prediction results.
Comparison of toxicity among female and male active politicians in social media
(Tartu Ülikool, 2024) Nesipoglu, Devrim; Kangur, Uku, juhendaja; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Toksilisuse analüüs sotsiaalmeedias on vaenu õhutava käitumise ja diskursuse mõistmisel kriitilise tähtsusega. Selle magistritöö eesmärk on võrrelda toksilisuse taset internetipõhises sotsiaalmeedia diskursuses aktiivsete Ameerika mees- ja naispoliitikute näitel. Uuringus kasutatakse mitmekülgset masinõppe lähenemist ja loomuliku keele töötlemise (NLP) tehnikaid. Töös mõõdetakse poliitikute postituste ja kommentaaride sentimenti ning kasutatakse toksilisuse tuvastamise mudeleid, mis klassifitseerivad teksti toksiliseks või mittetoksiliseks. Lisaks eraldatakse andmed meeste ja naiste kategooriatesse, võimaldades soopõhist toksilisuse võrdlust. Seejärel rakendatakse statistilist analüüsi, et hinnata ja võrrelda kahe rühma toksilisuse tasemeid, valgustades võimalikke soopõhiseid erinevusi veebidiskursuses. Tulemuste visualiseerimise ja tõlgendamise kaudu soovime aidata mõista toksilisuse mustreid sotsiaalmeedias poliitilise kommunikatsiooni ja soolise dünaamika osas.
Data Analytics for Estimating the Disposition Effect
(Tartu Ülikool, 2021) Talpsepp, Tõnn; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Evaluating the Impact of COVID-19 on People’s Perception of Travel Safety by Analysing Tweets
(Tartu Ülikool, 2023) Altmets, Anna-Liisa; Banerjee, Somnath, juhendaja; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The COVID-19 pandemic not only cost human lives but also harmed industries like tourism which adds valuable contributions to the GDP of many countries. The pandemic affected global tourism in several ways, such as fewer flights, cancellations, lockdowns and restrictions, etc. This thesis studies COVID-19's impact on people's perception of travel safety leveraging sentiment analysis. Travel-related social media data was collected from Twitter and divided by the severity of the pandemic and the tweets volume of the regions to study the impact and patterns. For analysing data, a RoBERTa-base pretrained sentiment analysis model for tweets was employed. Sentiment scores over time were compared to understand the general trends. Although most of the tweets were neutral, there was an evident change in the proportion of negative tweets to positive. A word frequency was also verified during different periods in this work. Virus-related words were frequently used in positive and negative tweets. The study reveals that people cancelled or postponed their trips due to risks caused by the pandemic.
Evaluation of alternative weighting methods for the selection of portfolio optimization model
(Tartu Ülikool, 2023) Rahimov, Nabi; Mammadov, Ismayil; Eratalay, Mustafa Hakan, juhendaja; Sharma, Rajesh, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Explanatory and Predictive Modelling in the Study of Overweight and Obesity: The Example of Health Behaviour Among Estonian Adult Population
(Tartu Ülikool, 2022) Gross, Toomas; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Fighting misinformation in the digital age: a comprehensive strategy for characterizing, identifying, and mitigating misinformation on online social media platforms
(2023-09-25) Sharma, Shakshi; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond
Veebipõhiste sotsiaalmeediaplatvormide, nagu Twitter ja Facebook, esilekerkimine on hõlbustanud valeteabe ülemaailmset levitamist, soodustades sotsiaalse hirmu, ärevuse ja majandusliku kahju kasvu. Lõputöö uurib mitmekülgset lähenemisviisi desinformatsiooniga võitlemiseks digiajastul, keskendudes kolmele põhidimensioonile: valeinformatsiooni sisu tuvastamine, raamistiku väljatöötamine valeinformatsiooni levitajate tuvastamiseks, ja tõhusate desinformatsioonivastaste meetmete rakendamine. Esiteks on meie väljapakutud postituste iseloomustamise meetodi eesmärk mõista kuulujuttudest ja mittekuulujuttudest postituste tunnuseid, et tuvastada postitajate kognitiivne tegevus ja desinformatsiooni levitamise motiivid. Sotsiaalmeediapostituste omaduste põhjalik uurimine aitab teadlaskonnal tuvastada ja vältida desinformatsiooni. Teiseks ei ole varasemad meetodid kahtlaste või pahatahtlike kasutajate ja desinformatsiooni tuvastamiseks Twitteris ja teistel sarnastel platvormidel piisavalt kaalunud kasutajatasandil toimuvat tuvastamist. Ühe postituse põhjal kasutaja kuulujuttude levitajaks liigitamisest ei piisa. Meie panus sellesse valdkonda on klassifitseerimisraamistik, mis ühendab parema lähenemisviisi väljatöötamiseks mitmed postitused ja võrguteabe. Kolmandaks on olemasolevad sotsiaalmeedias desinformatsiooni leviku piiramise lähenemisviisid kohati piiratud, näiteks puudub väline modereerimine ja süsteem tugineb rangetele eeldustele. Esitame automatiseeritud lahenduse valeinformatsiooni suuremahuliseks ümberlükkamiseks, kasutades selleks sotsiaalmeedia andmeid ja kureeritud kontrollitud faktidega andmehoidlaid. Eelkõige keskendutakse selles aspektis Twitteri platvormile ja COVID-19 väärinfole, uurides kahte teineteist täiendavat lähenemisviisi.
Forecasting Bicycle Demand: Bologna Case Study
(Tartu Ülikool, 2020) Davtyan, Taron; Sharma, Rajesh, juhendaja; Bertini, Flavio, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Although there are a large number of academical studies conducted about demand forecasting in docked bike-sharing programs, there is scarce literature on the dockless bikesharing programs and especially in forecasting demand using a deep learning approach. Dockless bike-sharing programs have been growing rapidly during the past few years and having a model that can accurately predict bike usage is becoming essential for bikesharing companies and governmental institutions. This research paper aims to develop a model to forecast the usage of private bicycles with a deep learning approach and fill the research gap mentioned above. For predicting the number of rides, long short-term memory (LSTM) neural networks model was developed. The model was used to predict bike usage for 30-minute and 60-minute intervals. Besides the historical number usage of bikes, the prediction model considers air temperature, precipitation amount, and national holidays. The study results suggest that prediction with the LSTM model gives a more accurate outcome than more widely used machine learning algorithms such as linear regression, Random Forrest, and XGBoost. LSTM model that was developed by this study can be used to predict the utilization of bike lanes, which can be essential for governmental institutions and can also help bike-sharing companies to distribute bikes across the city to provide more convenient experience to the users.
Gender-based segregation in company boards and well-being
(Tartu Ülikool, 2020) Kadri, Oluwagbemi Omobolanle; Sharma, Rajesh, juhendaja; Kungas, Peep, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Segregation is an act of division from a supreme body to smaller groups because of the characteristics of the body. In order terms among humans, we can refer to it as an unwarranted detachment or separation resulting in traits a person possesses; for example, gender, occupation, race, resident, income, religion, age, etc. Analyses based on segregation impact in our society have increased over the years. It has spawned enormous controversial discussions in our modern-day world; and elicited several researchers’ interests to identify the origin of segregation. In this thesis, we investigated if gender and age segregation exist in Estonian companies’ boards and its relationship with the labour market. In addition, we examine if it leads to high credit risks and a negative correlation to the well-being of Estonian society. The key measurement factors for comparison and drawing conclusions are the unemployment rate measured as the labour market, financial key performance indicators measured as credit risk, a well-deprivation index measured as well-being and segregation indexes from ‘SCube’ data model measured as segregation. ‘SCube’ originated from a model created by researchers at the University of Pisa, it uses a data science framework to deal with the problem of social and occupational segregation. Analysis from the ‘SCube’ data-set will be measured with segregation indexes ranging from 0 to 1 in accordance to this range high level of segregation means high value of the segregation index meaning a value close to 1. The Estonian statistics ready-made data-set is used in conjunction with the data-set from ‘SCube’ model to examine and draw conclusions of the occupational segregation problem discussed in this work. In addition, statistical techniques correlation and causal inference are used to determine the relationship and causal effects between segregation and the various factors.
Market manipulation in cryptocurrencies through social media: the role of influencers
(Tartu Ülikool, 2023) Rahimov, Kamran; Rahimov, Elchin; Lapitskaya, Darya, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Sharma, Rajesh, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Measuring Corporate Reputation through Online Social Media: A case study of the Volkswagen Emission Scandal
(Tartu Ülikool, 2020) Odeyinka, Olubunmi T; Sharma, Rajesh, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Reputation, a priceless but yet essential factor in a company’s success, influences a company’s customers, employees, and even the public relations with the company. Therefore, most companies consider reputation building as a priority in their daily interaction with their staff and the public. Such companies that successfully build a good reputation, which attracted investors, staff, and goodwill of the public sometimes run into issues or events (i.e. self-inflicted or not) that can result in a significant change in their reputation. The series of events that happen after a reputation tarnishing event includes more related news article releases, comments in message boards, and an increase in the number of people talking about the event on social media. In this thesis, we analyzed the company’s online media reputation (i.e. online media sentiment) and compared it with their financial reputation (i.e. stock price and volume) using the Volkswagen (VW) emission scandal as a case study. We did this by computing the monthly correlation, weekly correlation, rolling correlation, partial correlation, trend analysis, and brought out the context of a discussion with a word clouds and we tested our hypothesis of a relationship between online media sentiment and stock values using the Granger causality test. This analysis was done not just on VW online media sentiment and stocks but also on Ford, Toyota, and Audi as control cases for the period under analysis (i.e. VW scandal period). The result shows that during the heat of the scandal the company that is involved (i.e. VW) is the one that presents their sentiment result to be a measure of their reputation only in the media sources that had their context based on the scandal(i.e. news and Twitter) and with a stronger relationship in the weeks of major scandalous news or events. This relationship is not so for other substitute companies’ media dataset like Ford and Toyota whose media context and sentiment were not influenced by the VW scandal events. While Audi, a subsidiary of VW, and our third control case presented sentiment to be a relative measure of the corporate reputation in the news dataset because that is the only dataset of it that had the VW emissions scandal as one of its main focal points of discussion during the scandal timeline.
Mining social well-being using mobile data
(2023-06-08) Goel, Rahul; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond
Mobiilsed andmed, nagu kõneandmete kirjed (CDR) ja digitaalsed andmed, loovad suure hulga andmeid, mis sisaldavad väärtuslikku teavet inimeste käitumise kohta. Käesolevas lõputöös keskendume ühiskonna heaolu kolmele tahule. Esiteks pakume välja kaks mobiilsusepõhise SIR-mudeli versiooni, (i) täielikult segatud ja (ii) keeruliste võrkude jaoks, mis võtavad arvesse CDR-i tegelikke interaktsioone. See töö on inspireeritud eeldusest, et mõne epideemia pandeemiaks muutumise peamine põhjus on globaalne seotus, mis muudab lihtsamaks suurema geograafilise piirkonna, sageli globaalse, mõjutamise. Lisaks ei ole rahvastiku jaotus, inimeste liikuvus ja sotsiaalne sidusus kogu maailmas ühtlane, mis mängib kriitilist rolli. Kasutasime oma mudelit COVID-19 juhtumite prognoosimiseks Eestis ja Prantsusmaal Rhône-Alpes. Teiseks uurime CDR-andmete abil ühiskondlikku segregatsiooni Eestis. Meie tulemused viitavad sellele, et (i) Eestis esineb sooline segregatsioon ja selle jäljed on nähtavad nii inimeste helistamisaegades, vanuserühmade ühenduvuses, eelistatud suhtluskeeles kui ka maakonnas; (ii) Peamised töötavad isikud (st (25–54) vanuserühm) ja vanurid (s.o (64–100) vanuserühm) on rohkem segregeeritud; (iii) Eesti- ja venekeelsed isikud on keelepõhiselt eraldatud. Kolmandaks uurime sotsiaal-majanduslike tingimuste (SEC) ennustamiseks mobiilirakenduste (nt Twitter ja Facebook) digitaalseid jälgi. Need tingimused hõlmavad haridust, sugu, vaesust, tööhõivet ja muid tegureid. Seetõttu on usaldusväärne ja täpne teave sotsiaaluuringute ja valitsuse politseitöö jaoks ülioluline. Rakenduste kasutusmustreid kasutades suudab meie parim mudel hinnata majanduslikke, hariduslikke ja demograafilisi näitajaid (saavutades R-ruudu skoori kuni 0,66). Lisaks anname aru nende mudelite seletatavuse kohta, et teha kindlaks prognoosimise olulised tunnused. Avastame, et mobiilirakenduste kasutusmustrid võivad paljastada sotsiaalmajanduslikke erinevusi.
Mobility Pattern Analysis using CDR: A Case Study of Estonian Public Holidays in January & February
(Tartu Ülikool, 2023) Koldekivi, Laura Liisa; Sharma, Rajesh, juhendaja; Aasa, Anto, juhendaja; Goel, Rahul, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
With the rise of globalization and the growth of urban populations, mobility patterns have become a key factor in shaping our cities and communities. This study explores people’s mobility patterns during public holidays in Estonia using Call Data Records (CDR) data. Specifically, the study investigates mobility patterns at three different location levels: top locations, home municipality, and home county. The CDR dataset used in this study contains approximately 56M records and 499K distinct callers during January and February of 2018. The results indicate a correlation between public holidays and mobility patterns at all three location levels. People are less likely to stay in their top locations on both holidays, particularly in densely populated urban cities of Estonia, such as Tallinn, Tartu, and Pärnu. Additionally, people tend to spend their holidays in another municipality, with Hiiumaa island residents exhibiting the highest mobility and Ida-Viru County showing the most significant difference in mobility between the two holidays. The study also found that on a county level, people are more likely to deviate from their usual routines on New Year’s Day than on Independence Day. Overall, the results suggest that New Year’s Day alters mobility patterns more than Independence Day and the average mobility. These results are beneficial for urban planning and resource allocation during the holidays.
Predicting innovating companies in Estonia by analysing manufacture companies website data
(Tartu Ülikool, 2020) Sõna, Sander; Vahter, Priit, juhendaja; Sharma, Rajesh, juhendaja; Masso, Jaan, juhendaja; Tartu Ülikool. Majandusteaduskond; Tartu Ülikool. Sotsiaalteaduste valdkond
Predicting stock price based on media monitoring
(Tartu Ülikool, 2020) Aasmaa, Ardi; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Using automated systems for finding investment ideas becomes more popular every year and the models are getting more complex. In recent years a lot of studies have been conducted that have researched the possibilities of using social media sentiment as input for stock prediction models. However, the results have been contradicting as the problem is complex. In this research, data was collected from Twitter about Standard Poor’s 100 companies over a period of six months. Also, financial data with one minute interval was collected from Alpha Vantage. Five different machine learning algorithms were used to predict maximum profit and maximum loss for the prediction horizon of five trading days. It was investigated whether adding social media based features to financial data based features would improve the results and if so, then tweets from what type of users would give the highest information gain. It was found out that adding social media data as input is beneficial for both, predicting maximum loss and maximum profit. For the explainability part, Shap library was used. As found out, features extracted from financial data were most important. For social media based features, most information was gained from tweets posted by news agencies and by users having relatively few followers.
Predicting stock return and volatility with machine learning and econometric models— a comparative case study of the Baltic stock market
(Tartu Ülikool, 2021) Nõu, Anders; Sharma, Rajesh, juhendaja; Eratalay, Mustafa Hakan, juhendaja; Lapitskaya, Darya, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Predicting the stock market is a widely researched area of study that is a challenging task. The nature of the problem lies in correctly forecasting the direction and the magnitude of the stock market movement. The severity of the problem exists due to the stock market being impacted by a multitude of factors. There are numerous ways to analyse the stock market and make appropriate investment decisions, but it is challenging to decide the best approach. Here we show which approach is more effective in predicting the returns and volatility of the Baltic stock market: the machine learning or econometric approach. There is a low amount of research on using machine learning or econometric models to predict the Baltic stock market. However, there are no comparative researches that offer a fair comparison between the different approaches for the Baltic stock market. Regarding results, the lowest symmetric mean absolute percentage error for the support vector regression model is 61.90%, and for the autoregressive moving average model, it is 165.43%. The lowest symmetric mean absolute percentage error for GARCH is 51.05%, and for the GARCH-ANN model, it is 61.65%. Overall, the machine learning models outperform the econometric models in most of the evaluated metrics. However, the econometric models’ results are comparable to the machine learning models’ results in most cases.
Prediction of a movie’s box office using pre-release data
(Tartu Ülikool, 2020) Bondarenko, Stanislav; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
It’s difficult to overestimate the impact of the film industry in our lives, it expands our knowledge about the world and culture and entertains. Going to the cinema has become an important leisure activity. Moreover, the total worldwide box office in 2018 hit a significant amount of $41B. This is not surprising as only in 2018 there were released 11,911 feature-length films worldwide. The box office generated from cinema ticket sales is the main source of profit for widely released movies. However, not all movies are successful in terms of profit when the cost of production is compared with the total box office. 78% of movies released worldwide are not profitable and 35% of profitable movies earn 80% of the total profit. Seeing the importance of theatrical screenplays and tough competition for the profit made, we want to be able to predict how successful a movie is going to be and whether it is worth taking the risk of investment. Only pre-release available data is used to be able to make a prediction at the earliest stages. We went through several stages typical for data mining and machine learning to obtain possibly the biggest and feature-rich dataset used in box office gross prediction. We use neural networks and gradient boosting machines to be able to predict the absolute box office gross, predict within which range it is likely to be, and whether a movie will be profitable, and the results obtained are very competitive in the domain.
Study of aggressive behavior on social media
(Tartu Ülikool, 2023) Kvirikashvili, Ketevani; Mane, Swapnil, juhendaja; Sharma, Rajesh, juhendaja; Kundu, Suman, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Recently, the expression of aggression in social networks has increased a lot, which also causes a lot of adverse effects, such as mental health problems or some other controversies. Hence we perform the first ever user aggressive behavior analysis on Twitter social media official microblogging site, which has no restriction on aggressive behavior. Using the proposed pipeline, we study the user’s aggressive behavior. The pipeline is based on three stages such as data collection, aggression detection, and user profiling. In this study, we detailed analyzed the aggressive behavior of users are depends on their aggressive feeds and events. Further, our analysis revealed that user engagement is higher in aggressive posts.
Studying online social media engagement in CIS countries during protests, mass demonstrations and war
(2023-10-13) Slobozhan, Ivan; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond
Lõputöös uuriti proteste ja konflikte ning sellel uurimistööl on kaks olulisemat teaduslikku panust. Esiteks keskendume kahele Sõltumatute Riikide Ühenduse (SRÜ) riigile (Ukrainale ja Valgevenemaale), mis pole seni eriti paljude uurijate tähelepanu keskmes olnud. Palju on uuritud lääneriike, kus valdav enamus räägib inglise keelt. Samuti on teatud määral uuritud araabia maid (näiteks Egiptust). Teiseks keskendusime uuringut läbi viies mitte Twitterile, mis on protestide uurimiseks levinuim platvorm, vaid alternatiivsetele platvormidele, nagu Facebook ja Telegram. Need platvormid valisime seetõttu, et need on SRÜ riikides populaarsemad. Lõputöö hõlmab kolme täpsemat uurimisteemat ning uuringu käigus kasutasime erinevaid arvutuslikke lähenemisi, nagu näiteks NLP, AI ja sotsiaalvõrgustike analüüs. Esmalt uurisime keelekasutust, et analüüsida Euromaydani Facebookigrupi liikmete käitumist enne Ukrainas toimunud Euromaidani revolutsiooni. Analüüs paljastas, et kasutajad muutsid oma keelekasutust, mille põhjuseks on meie hinnangul poliitilised ja ajaloolised faktorid. Teiseks uurisime arvukate konfliktiga seotud gruppide käitumist. Täpsemalt keskendusime mustritele erinevates Telegrami suhtluskeskkondades (kanalid, grupid ja kohalikud vestlused) Valgevene 2020. aasta protestide ajal. Lõputöö kolmanda teema raames viisime läbi kahe vastandliku poolega joonduvate organisatsioonide käitumise võrdleva uuringu. Peamiselt keskendusime Ukraina ja Venemaa kõige tuntumate ja mõjukamate massimeediaväljaannete tegevustele ja strateegiatele ajaperioodil, mil Venemaa 2022. aastal Ukrainasse tungis. Eelkõige olime huvitatud sellest, kuidas need väljaanded kujundasid konflikti kajastades oma propagandategevust, mis mõjutas inimeste käitumist.
The Effects of COVID-19 on Consumption of Animal and Plant-based Food: An Analysis of Twitter Data
(Tartu Ülikool, 2021) Guliyev, Musa; Sharma, Rajesh, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
This study finds the the impact of COVID-19 on the consumption of meat and plant-based food by analyzing the opinions of the supporters of both diets on Twitter. The study uses around 10 million tweets gathered from November 2019 to June 2020 to cover 3 time periods: Prepandemic, when COVID-19 was spreading silently (November 2019 - January 2020), Transition, when governments around the world started mass information spread and took action (February - March 2020), and Pandemic, when almost all people lived under some sort of restriction (April - June 2020). The study also analyzes the opinions of the users on both types of food and groups them into 2 groups: meat-lovers and veggies. Tweets of each group in each time period are analyzed and compared to see the change over time. The results show that, neither of the groups were more popular than the other in all time periods. However, there is a change when tweets are grouped by topics. Tweets about diets became more popular during pandemic, while the number of very positive and very negative tweets about animal-based food increased. In addition, towards the pandemic, both groups became more in contact with each other, despite being previously isolated, and started paying more attention to risks related to meat and links with the virus. Finally, the pandemic has increased negativity in veggies’ tweets on food while not affecting meat-lovers.